R Notebook: estimation of impact of arguments on gas costs; for Gas Cost Estimator; for a single given env
In this script we conduct the estimation for the
measure_arguments approach.
PROGRAMS=pg_arguments_full5_c200_opc15x2 SAMPLESIZE=50 NSAMPLES=1`.
Expected a result file revm_pg_arguments_full5_c200_opc15x2_.csv.
# the programs file is too large to be placed in github
programs = read.csv(paste("../../local/", program_set_codename, ".csv", sep=""))
results = load_data_set(env, program_set_codename, measurement_codename)
# besu may have additional columns with gc stats
results = results[, c("program_id", "sample_id", "run_id", "measure_total_time_ns", "measure_total_timer_time_ns", "env")]
# TODO geth short-circuits zero length programs, resulting in zero timing somehow. Drop these more elegantly, not based on measure_total_time_ns
results = results[which(results$measure_total_time_ns != 0), ]
all_envs = c(env)measurements = sqldf("SELECT opcode, op_count, arg0, arg1, arg2, sample_id, run_id, measure_total_time_ns, env, results.program_id
FROM results
INNER JOIN
programs ON(results.program_id = programs.program_id)
")
measurements$opcode = factor(measurements$opcode, levels=unique(programs$opcode))
head(measurements)## opcode op_count arg0 arg1 arg2 sample_id run_id measure_total_time_ns env
## 1 ADD 0 25 27 NA 0 1 579 revm
## 2 ADD 15 25 27 NA 0 1 628 revm
## 3 ADD 30 25 27 NA 0 1 696 revm
## 4 ADD 0 14 9 NA 0 1 576 revm
## 5 ADD 15 14 9 NA 0 1 625 revm
## 6 ADD 30 14 9 NA 0 1 662 revm
## program_id
## 1 ADD_0
## 2 ADD_1
## 3 ADD_2
## 4 ADD_3
## 5 ADD_4
## 6 ADD_5Remove outliers if needed.
# Extracts all OPCODEs from the `programs` data frame of the given arity (args taken off the stack).
extract_opcodes <- function(arity) {
if (!missing(arity)) {
if (arity == 0) {
programs = programs[which(is.na(programs$arg0) & is.na(programs$arg1) & is.na(programs$arg2)), ]
}
if (arity == 1) {
programs = programs[which(!is.na(programs$arg0) & is.na(programs$arg1) & is.na(programs$arg2)), ]
}
if (arity == 2) {
programs = programs[which(!is.na(programs$arg1) & is.na(programs$arg2)), ]
}
if (arity == 3) {
programs = programs[which(!is.na(programs$arg2)), ]
}
}
unique(programs$opcode)
}if ( (!removed_outliers) && (!removed_outliers_2)) {
boxplot(measure_total_time_ns ~ opcode, data=measurements[which(measurements$env == env), ], las=2, outline=TRUE, log='y', main=paste(env, 'all'))
}if (removed_outliers) {
par(mfrow=c(length(all_envs)*2, 1))
# before
boxplot(measure_total_time_ns ~ opcode, data=measurements[which(measurements$env == env), ], las=2, outline=TRUE, log='y', main=paste(env, 'all'))
measurements = remove_outliers(measurements, 'measure_total_time_ns', FALSE)
# after
boxplot(measure_total_time_ns ~ opcode, data=measurements[which(measurements$env == env), ], las=2, outline=TRUE, log='y', main=paste(env, 'no_outliers'))
}all_opcodes = extract_opcodes()
nullary_opcodes = extract_opcodes(0)
unary_opcodes = extract_opcodes(1)
binary_opcodes = extract_opcodes(2)
ternary_opcodes = extract_opcodes(3)
div_opcodes = c('DIV', 'MOD', 'SDIV', 'SMOD')measurements$expensive = NA
measurements[which(measurements$opcode %in% div_opcodes), ]$expensive =
measurements[which(measurements$opcode %in% div_opcodes), ]$arg0 >
measurements[which(measurements$opcode %in% div_opcodes), ]$arg1
# remember that argX is the byte-size of the argument in these measurements
measurements[which(measurements$opcode == 'ADDMOD'), ]$expensive =
8**measurements[which(measurements$opcode == 'ADDMOD'), ]$arg0 +
8**measurements[which(measurements$opcode == 'ADDMOD'), ]$arg1 >
8**measurements[which(measurements$opcode == 'ADDMOD'), ]$arg2
measurements[which(measurements$opcode == 'MULMOD'), ]$expensive =
measurements[which(measurements$opcode == 'MULMOD'), ]$arg0 +
measurements[which(measurements$opcode == 'MULMOD'), ]$arg1 >
measurements[which(measurements$opcode == 'MULMOD'), ]$arg2if (removed_outliers_2) {
measurements = remove_compare_outliers(measurements, 'measure_total_time_ns', all_envs)
}
Detailed view
This is massive and detailed overview on the impact of arguments.
Because of the number of charts, only op count = 30 is
eligible. Feel free to change it, but that should not be anyhow more
informative. The visualizations do not guarantee that all dependencies
are clearly seen. Especially for binary and ternary opcodes where
impacts of arg0, arg1 and arg2 are mixed. But if a dependency is
graphically noticeable that you should expect also statistical
dependency.
for (env in all_envs) {
for (opcode in unary_opcodes) {
# plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
# title(main = paste(env, opcode, 'arg0', 'opcount 0'))
# plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
# title(main = paste(env, opcode, 'arg0', 'opcount 15'))
plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
title(main = paste(env, opcode, 'arg0', 'opcount 30'))
}
for (opcode in binary_opcodes) {
# plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
# title(main = paste(env, opcode, 'arg0', 'opcount 0'))
# plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
# title(main = paste(env, opcode, 'arg0', 'opcount 15'))
plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
title(main = paste(env, opcode, 'arg0', 'opcount 30'))
# plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
# title(main = paste(env, opcode, 'arg1', 'opcount 0'))
# plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
# title(main = paste(env, opcode, 'arg1', 'opcount 15'))
plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
title(main = paste(env, opcode, 'arg1', 'opcount 30'))
}
for (opcode in ternary_opcodes) {
# plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
# title(main = paste(env, opcode, 'arg0', 'opcount 0'))
# plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
# title(main = paste(env, opcode, 'arg0', 'opcount 15'))
plot(measure_total_time_ns ~ arg0, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
title(main = paste(env, opcode, 'arg0', 'opcount 30'))
# plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
# title(main = paste(env, opcode, 'arg1', 'opcount 0'))
# plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
# title(main = paste(env, opcode, 'arg1', 'opcount 15'))
plot(measure_total_time_ns ~ arg1, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
title(main = paste(env, opcode, 'arg1', 'opcount 30'))
# plot(measure_total_time_ns ~ arg2, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 0), ], pch=0, col='darkgreen')
# title(main = paste(env, opcode, 'arg2', 'opcount 0'))
# plot(measure_total_time_ns ~ arg2, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 15), ], pch=1, col='red')
# title(main = paste(env, opcode, 'arg2', 'opcount 15'))
plot(measure_total_time_ns ~ arg2, data=measurements[which(measurements$env == env & measurements$opcode == opcode & measurements$op_count == 30), ], pch=5, col='blue')
title(main = paste(env, opcode, 'arg2', 'opcount 30'))
}
}
Models
Notes: 1. Outliers need to be removed if detected 2. The
argX:op_count interactions measure the impact on the OPCODE
3. The argX are just auxiliary variables added to exclude
the effect of cheaper/more expensive PUSHes. We only want to extract the
effect of the argument on the measured OPCODE repeated
op_count times.
# Every `arg` coefficient represents the impact of the argument's byte size growing by 1.
# We treat as impactful the arguments where p-value is effectively zero. The previous approach was:
# Treat as impactful the arguments, where:
# 1. The estimate is significant with confidence 0.001
# 2. The increase of arg's byte size by 1 will increase the cost by more than 1%
# but it turned out to be much less stable in practice.
p_value_thresh = 1e-30
# p_value_thresh = 0.001
impact_ratio = 0.00
# impact_ratio = 0.01
arg_lm <- function(df, opcode, env, formula) {
data = df[which(df$opcode==opcode & df$env==env), ]
lm(formula, data=data)
}
# Adds the results from the estimated `model` to the `results_df` data frame.
# You need to provide the corresponding `opcode`, `env` and `arity`.
# `results_df` is assumed to have the columns as the `first_pass` data frame has (see below)
add_arg_results <- function(model, opcode, env, results_df, arity) {
stopifnot(arity > 0)
all_coefficients = summary(model)$coefficients
arg_coefficients = all_coefficients[!(row.names(all_coefficients) %in% c("op_count", "(Intercept)", "arg0", "arg1", "arg2")),]
pure_op_count_coeff = all_coefficients["op_count", 1]
# will be filled if any is impacting
args_ns = c(NA, NA, NA)
# will be always if arg present
args_ns_raw = c(NA, NA, NA)
args_ns_p = c(NA, NA, NA)
if (arity == 1) {
# there's only one arg coefficient here, silly R forces us to take a special case path...
has_significant = arg_coefficients[4] < p_value_thresh
if (has_significant) {
coefficient_impact = abs(arg_coefficients[1])
has_impacting = has_significant & coefficient_impact > pure_op_count_coeff * impact_ratio
} else {
has_impacting = FALSE
}
if (has_impacting) {
args_ns[1] = arg_coefficients[1]
}
args_ns_raw[1] = arg_coefficients[1]
args_ns_p[1] = arg_coefficients[4]
} else {
significant = arg_coefficients[, 4] < p_value_thresh
has_significant = length(which(significant)) > 0
coefficient_impact = abs(arg_coefficients[, 1])
can_impact = significant & coefficient_impact > pure_op_count_coeff * impact_ratio
has_impacting = length(which(can_impact)) > 0
args_ns[which(can_impact)] = arg_coefficients[which(can_impact), 1]
args_ns_raw[1:arity] = arg_coefficients[1:arity, 1]
args_ns_p[1:arity] = arg_coefficients[1:arity, 4]
}
# NAs for the "expensive" arg columns. See above for the columns layout
results_df[nrow(results_df) + 1, ] = c(opcode, env, has_significant, has_impacting, pure_op_count_coeff, args_ns, NA, args_ns_raw, NA, args_ns_p, NA)
return(results_df)
}
# Adds the results from the estimated `model` to the `results_df` data frame, where the model is
# specifically the one gauged towards the "division" OPCODEs like `DIV`.
# See also `add_arg_results`
add_arg_expensive_results <- function(model, opcode, env, results_df, arity) {
stopifnot(arity > 0)
all_coefficients = summary(model)$coefficients
pure_op_count_coeff = all_coefficients["op_count", 1]
expensive = NA
# there's only one arg coefficient here, silly R forces us to take a special case path...
has_significant = all_coefficients['op_count:expensiveTRUE', 4] < p_value_thresh
if (has_significant) {
coefficient_impact = abs(all_coefficients['op_count:expensiveTRUE', 1])
has_impacting = has_significant & coefficient_impact > pure_op_count_coeff * impact_ratio
} else {
has_impacting = FALSE
}
if (has_impacting) {
expensive = all_coefficients['op_count:expensiveTRUE', 1]
}
expensive_raw = all_coefficients['op_count:expensiveTRUE', 1]
expensive_p = all_coefficients['op_count:expensiveTRUE', 4]
results_df[which(results_df$opcode == opcode & results_df$env == env), 'expensive_ns'] = expensive
results_df[which(results_df$opcode == opcode & results_df$env == env), 'expensive_ns_raw'] = expensive_raw
results_df[which(results_df$opcode == opcode & results_df$env == env), 'expensive_ns_p'] = expensive_p
return(results_df)
}
# Goes through all the families of OPCODEs and fits and displays their respective `measure_arguments`
# models.
# Results are gathered in a common `results_df` data frame.
analyze_for_env <- function(df, results_df, env) {
for (opcode in unary_opcodes) {
model = arg_lm(df, opcode, env, measure_total_time_ns ~ op_count + arg0 + arg0:op_count)
print(c(opcode, env))
print(summary(model))
results_df = add_arg_results(model, opcode, env, results_df, 1)
}
for (opcode in binary_opcodes) {
model = arg_lm(df, opcode, env, measure_total_time_ns ~ op_count + arg0 + arg1 + arg0:op_count + arg1:op_count)
print(c(opcode, env))
print(summary(model))
results_df = add_arg_results(model, opcode, env, results_df, 2)
}
for (opcode in ternary_opcodes) {
model = arg_lm(df, opcode, env, measure_total_time_ns ~ op_count + arg0 + arg1 + arg2 + arg0:op_count + arg1:op_count + arg2:op_count)
print(c(opcode, env))
print(summary(model))
results_df = add_arg_results(model, opcode, env, results_df, 3)
}
for (opcode in div_opcodes) {
model = arg_lm(df, opcode, env, measure_total_time_ns ~ op_count + arg0 + arg1 + expensive:op_count)
print(c(opcode, env))
print(summary(model))
results_df = add_arg_expensive_results(model, opcode, env, results_df, 2)
}
for (opcode in c('ADDMOD', 'MULMOD')) {
model = arg_lm(df, opcode, env, measure_total_time_ns ~ op_count + arg0 + arg1 + arg2 + expensive:op_count)
print(c(opcode, env))
print(summary(model))
results_df = add_arg_expensive_results(model, opcode, env, results_df, 3)
}
return(results_df)
}This is the so-called “first-pass” at the estimation procedure, where
we estimated all possible argument impact variables for all OPCODEs. We
gather all the results in the first_pass table, inspect
this to see where the arguments turned out to be significantly impacting
the computation cost.
first_pass = data.frame(matrix(ncol = 17, nrow = 0))
colnames(first_pass) <- c('opcode', 'env', 'has_significant', 'has_impacting', 'estimate_marginal_ns',
'arg0_ns', 'arg1_ns', 'arg2_ns', 'expensive_ns',
'arg0_ns_raw', 'arg1_ns_raw', 'arg2_ns_raw', 'expensive_ns_raw',
'arg0_ns_p', 'arg1_ns_p', 'arg2_ns_p', 'expensive_ns_p')
first_pass = analyze_for_env(measurements, first_pass, env)## [1] "ISZERO" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.819 -3.962 -1.929 5.676 12.699
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.9756956 0.7355718 787.110 <0.0000000000000002 ***
## op_count 2.1569271 0.0379494 56.837 <0.0000000000000002 ***
## arg0 -0.0156510 0.0414011 -0.378 0.706
## op_count:arg0 0.0009636 0.0021376 0.451 0.652
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.12 on 473 degrees of freedom
## Multiple R-squared: 0.9643, Adjusted R-squared: 0.9641
## F-statistic: 4263 on 3 and 473 DF, p-value: < 0.00000000000000022
##
## [1] "NOT" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.090 -3.987 -2.054 3.968 15.911
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.2879039 0.7376285 783.983 <0.0000000000000002 ***
## op_count 2.1934399 0.0381841 57.444 <0.0000000000000002 ***
## arg0 -0.0111608 0.0381783 -0.292 0.77
## op_count:arg0 0.0003694 0.0019569 0.189 0.85
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.788 on 477 degrees of freedom
## Multiple R-squared: 0.9692, Adjusted R-squared: 0.969
## F-statistic: 5002 on 3 and 477 DF, p-value: < 0.00000000000000022
##
## [1] "CALLDATALOAD" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -70.21 -38.69 -19.15 37.73 155.71
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 839.35670324 6.41890920 130.763 <0.0000000000000002 ***
## op_count 2.83495687 0.33194607 8.540 <0.0000000000000002 ***
## arg0 0.00006530 0.00064051 0.102 0.919
## op_count:arg0 0.00003009 0.00003312 0.908 0.364
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 47.68 on 590 degrees of freedom
## Multiple R-squared: 0.3911, Adjusted R-squared: 0.388
## F-statistic: 126.3 on 3 and 590 DF, p-value: < 0.00000000000000022
##
## [1] "POP" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.4916 -4.2137 -0.2337 3.8883 17.0101
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 374.2432363 0.7547139 495.874 <0.0000000000000002 ***
## op_count 1.9920421 0.0390774 50.977 <0.0000000000000002 ***
## arg0 -0.0010542 0.0392292 -0.027 0.979
## op_count:arg0 -0.0007419 0.0020428 -0.363 0.717
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.435 on 533 degrees of freedom
## Multiple R-squared: 0.9525, Adjusted R-squared: 0.9522
## F-statistic: 3563 on 3 and 533 DF, p-value: < 0.00000000000000022
##
## [1] "MLOAD" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -83.22 -42.45 -17.03 38.48 161.42
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 855.356396346 7.259653543 117.823 <0.0000000000000002 ***
## op_count 3.475402738 0.373856370 9.296 <0.0000000000000002 ***
## arg0 -0.000477746 0.000708767 -0.674 0.501
## op_count:arg0 -0.000002212 0.000036667 -0.060 0.952
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 51.97 on 581 degrees of freedom
## Multiple R-squared: 0.4039, Adjusted R-squared: 0.4008
## F-statistic: 131.2 on 3 and 581 DF, p-value: < 0.00000000000000022
##
## [1] "JUMPI" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.25 -15.67 -10.87 22.73 47.11
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 557.6079528 2.7441217 203.201 <0.0000000000000002 ***
## op_count 3.9488737 0.1426012 27.692 <0.0000000000000002 ***
## arg0 0.0108371 0.1485570 0.073 0.942
## op_count:arg0 0.0002968 0.0077169 0.038 0.969
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.24 on 524 degrees of freedom
## Multiple R-squared: 0.8504, Adjusted R-squared: 0.8495
## F-statistic: 992.6 on 3 and 524 DF, p-value: < 0.00000000000000022
##
## [1] "DUP1" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.949 -4.828 -2.291 4.747 23.868
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.812699 0.947085 611.152 <0.0000000000000002 ***
## op_count 2.324822 0.048384 48.049 <0.0000000000000002 ***
## arg0 -0.016290 0.048576 -0.335 0.738
## op_count:arg0 0.002327 0.002487 0.935 0.350
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.168 on 482 degrees of freedom
## Multiple R-squared: 0.957, Adjusted R-squared: 0.9568
## F-statistic: 3579 on 3 and 482 DF, p-value: < 0.00000000000000022
##
## [1] "DUP2" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.105 -4.068 -1.758 3.974 24.885
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.3109367 0.7274979 794.931 <0.0000000000000002 ***
## op_count 2.3274369 0.0374468 62.153 <0.0000000000000002 ***
## arg0 -0.0276276 0.0384189 -0.719 0.472
## op_count:arg0 0.0008316 0.0019668 0.423 0.673
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.161 on 483 degrees of freedom
## Multiple R-squared: 0.969, Adjusted R-squared: 0.9688
## F-statistic: 5030 on 3 and 483 DF, p-value: < 0.00000000000000022
##
## [1] "DUP3" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.456 -3.522 -2.353 4.693 22.846
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.5685962 0.7516405 769.741 <0.0000000000000002 ***
## op_count 2.3260793 0.0394120 59.020 <0.0000000000000002 ***
## arg0 -0.0093672 0.0401027 -0.234 0.815
## op_count:arg0 -0.0008342 0.0021215 -0.393 0.694
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.106 on 485 degrees of freedom
## Multiple R-squared: 0.9691, Adjusted R-squared: 0.9689
## F-statistic: 5077 on 3 and 485 DF, p-value: < 0.00000000000000022
##
## [1] "DUP4" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.727 -4.054 -2.067 4.696 19.795
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.68066979 0.76783935 753.648 <0.0000000000000002 ***
## op_count 2.28679216 0.03972834 57.561 <0.0000000000000002 ***
## arg0 0.02335002 0.04042810 0.578 0.564
## op_count:arg0 0.00009093 0.00211170 0.043 0.966
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.274 on 477 degrees of freedom
## Multiple R-squared: 0.966, Adjusted R-squared: 0.9658
## F-statistic: 4517 on 3 and 477 DF, p-value: < 0.00000000000000022
##
## [1] "DUP5" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.730 -4.077 -1.489 4.001 18.622
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 577.9527723 0.7323031 789.226 <0.0000000000000002 ***
## op_count 2.3098371 0.0365195 63.249 <0.0000000000000002 ***
## arg0 0.0118503 0.0372156 0.318 0.750
## op_count:arg0 0.0002225 0.0018833 0.118 0.906
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.892 on 486 degrees of freedom
## Multiple R-squared: 0.9713, Adjusted R-squared: 0.9711
## F-statistic: 5483 on 3 and 486 DF, p-value: < 0.00000000000000022
##
## [1] "DUP6" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.481 -3.555 -1.979 4.245 12.470
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.06324541 0.71939831 803.537 <0.0000000000000002 ***
## op_count 2.31675716 0.03733607 62.051 <0.0000000000000002 ***
## arg0 -0.00284664 0.03737133 -0.076 0.939
## op_count:arg0 -0.00002326 0.00194023 -0.012 0.990
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.686 on 484 degrees of freedom
## Multiple R-squared: 0.9737, Adjusted R-squared: 0.9735
## F-statistic: 5975 on 3 and 484 DF, p-value: < 0.00000000000000022
##
## [1] "DUP7" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.835 -3.934 -1.961 4.219 27.352
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 582.690493 0.885051 658.369 <0.0000000000000002 ***
## op_count 2.275245 0.045313 50.212 <0.0000000000000002 ***
## arg0 0.009016 0.047627 0.189 0.850
## op_count:arg0 -0.001024 0.002415 -0.424 0.672
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.788 on 477 degrees of freedom
## Multiple R-squared: 0.9577, Adjusted R-squared: 0.9574
## F-statistic: 3596 on 3 and 477 DF, p-value: < 0.00000000000000022
##
## [1] "DUP8" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.208 -4.345 -2.370 4.429 21.466
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 586.398909 0.738918 793.591 <0.0000000000000002 ***
## op_count 2.269390 0.038540 58.884 <0.0000000000000002 ***
## arg0 0.042591 0.042119 1.011 0.312
## op_count:arg0 -0.001588 0.002206 -0.720 0.472
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.265 on 479 degrees of freedom
## Multiple R-squared: 0.965, Adjusted R-squared: 0.9648
## F-statistic: 4407 on 3 and 479 DF, p-value: < 0.00000000000000022
##
## [1] "DUP9" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.661 -3.482 -2.171 4.420 13.907
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.402120 0.676035 855.580 <0.0000000000000002 ***
## op_count 2.289866 0.035410 64.668 <0.0000000000000002 ***
## arg0 0.003972 0.036455 0.109 0.913
## op_count:arg0 -0.001678 0.001904 -0.881 0.379
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.554 on 475 degrees of freedom
## Multiple R-squared: 0.9737, Adjusted R-squared: 0.9735
## F-statistic: 5851 on 3 and 475 DF, p-value: < 0.00000000000000022
##
## [1] "DUP10" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.319 -4.365 -2.776 4.517 27.299
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.6341273 0.9514060 608.188 <0.0000000000000002 ***
## op_count 2.3133891 0.0482273 47.968 <0.0000000000000002 ***
## arg0 0.0235696 0.0480942 0.490 0.624
## op_count:arg0 -0.0004083 0.0024323 -0.168 0.867
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.266 on 483 degrees of freedom
## Multiple R-squared: 0.953, Adjusted R-squared: 0.9527
## F-statistic: 3262 on 3 and 483 DF, p-value: < 0.00000000000000022
##
## [1] "DUP11" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.454 -3.859 -2.408 4.488 16.565
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 591.439060 0.781988 756.327 <0.0000000000000002 ***
## op_count 2.348242 0.040411 58.109 <0.0000000000000002 ***
## arg0 0.004248 0.041638 0.102 0.919
## op_count:arg0 -0.001043 0.002114 -0.493 0.622
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.115 on 472 degrees of freedom
## Multiple R-squared: 0.9694, Adjusted R-squared: 0.9692
## F-statistic: 4977 on 3 and 472 DF, p-value: < 0.00000000000000022
##
## [1] "DUP12" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.205 -3.822 -1.992 4.013 16.806
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.3169483 0.6845829 844.773 <0.0000000000000002 ***
## op_count 2.2909562 0.0351509 65.175 <0.0000000000000002 ***
## arg0 -0.0112078 0.0362752 -0.309 0.757
## op_count:arg0 -0.0005016 0.0018653 -0.269 0.788
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.75 on 488 degrees of freedom
## Multiple R-squared: 0.9722, Adjusted R-squared: 0.9721
## F-statistic: 5698 on 3 and 488 DF, p-value: < 0.00000000000000022
##
## [1] "DUP13" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.397 -4.549 -2.486 4.304 23.257
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 582.537259 0.963433 604.647 <0.0000000000000002 ***
## op_count 2.340797 0.050008 46.809 <0.0000000000000002 ***
## arg0 -0.005407 0.048592 -0.111 0.911
## op_count:arg0 0.001144 0.002530 0.452 0.651
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.279 on 492 degrees of freedom
## Multiple R-squared: 0.9551, Adjusted R-squared: 0.9548
## F-statistic: 3488 on 3 and 492 DF, p-value: < 0.00000000000000022
##
## [1] "DUP14" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.829 -4.218 -2.664 4.627 24.766
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 583.0766300 0.8712332 669.254 <0.0000000000000002 ***
## op_count 2.2686351 0.0456314 49.717 <0.0000000000000002 ***
## arg0 0.0235125 0.0479081 0.491 0.624
## op_count:arg0 -0.0007934 0.0024936 -0.318 0.750
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.015 on 479 degrees of freedom
## Multiple R-squared: 0.9543, Adjusted R-squared: 0.954
## F-statistic: 3335 on 3 and 479 DF, p-value: < 0.00000000000000022
##
## [1] "DUP15" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.325 -4.859 -1.947 3.705 26.104
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.582807 1.062377 544.612 <0.0000000000000002 ***
## op_count 2.364935 0.053834 43.930 <0.0000000000000002 ***
## arg0 0.012556 0.055506 0.226 0.821
## op_count:arg0 -0.001400 0.002812 -0.498 0.619
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.546 on 489 degrees of freedom
## Multiple R-squared: 0.9501, Adjusted R-squared: 0.9498
## F-statistic: 3104 on 3 and 489 DF, p-value: < 0.00000000000000022
##
## [1] "DUP16" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.384 -3.809 -1.753 4.633 17.775
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 600.849715 0.744203 807.373 <0.0000000000000002 ***
## op_count 2.334641 0.038134 61.222 <0.0000000000000002 ***
## arg0 -0.009659 0.038250 -0.253 0.801
## op_count:arg0 0.001972 0.001951 1.011 0.313
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.873 on 478 degrees of freedom
## Multiple R-squared: 0.9727, Adjusted R-squared: 0.9726
## F-statistic: 5686 on 3 and 478 DF, p-value: < 0.00000000000000022
##
## [1] "ADD" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.526 -5.413 -3.237 6.706 15.789
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 579.9489447 1.3480367 430.217 <0.0000000000000002 ***
## op_count 2.8222034 0.0682553 41.348 <0.0000000000000002 ***
## arg0 -0.0248637 0.0532171 -0.467 0.641
## arg1 -0.0079579 0.0509653 -0.156 0.876
## op_count:arg0 0.0025971 0.0026808 0.969 0.333
## op_count:arg1 -0.0002471 0.0026353 -0.094 0.925
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.628 on 498 degrees of freedom
## Multiple R-squared: 0.9659, Adjusted R-squared: 0.9656
## F-statistic: 2825 on 5 and 498 DF, p-value: < 0.00000000000000022
##
## [1] "MUL" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.725 -4.737 -2.864 4.986 25.067
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 579.08604113 1.25571478 461.160 <0.0000000000000002 ***
## op_count 3.56906713 0.06511592 54.811 <0.0000000000000002 ***
## arg0 0.02170436 0.05023558 0.432 0.666
## arg1 -0.00371572 0.05153846 -0.072 0.943
## op_count:arg0 0.00126162 0.00258593 0.488 0.626
## op_count:arg1 0.00002024 0.00268670 0.008 0.994
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.377 on 488 degrees of freedom
## Multiple R-squared: 0.9793, Adjusted R-squared: 0.9791
## F-statistic: 4624 on 5 and 488 DF, p-value: < 0.00000000000000022
##
## [1] "SUB" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.196 -5.005 -2.747 5.427 30.466
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 579.5927620 1.3624858 425.394 <0.0000000000000002 ***
## op_count 2.7579557 0.0687780 40.099 <0.0000000000000002 ***
## arg0 -0.0069894 0.0538100 -0.130 0.897
## arg1 -0.0361458 0.0519204 -0.696 0.487
## op_count:arg0 -0.0001726 0.0027491 -0.063 0.950
## op_count:arg1 0.0006169 0.0026575 0.232 0.817
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.611 on 499 degrees of freedom
## Multiple R-squared: 0.9644, Adjusted R-squared: 0.964
## F-statistic: 2701 on 5 and 499 DF, p-value: < 0.00000000000000022
##
## [1] "DIV" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -207.79 -31.59 -4.31 20.08 355.00
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 580.582327 15.140643 38.346 <0.0000000000000002 ***
## op_count 6.598138 0.755205 8.737 <0.0000000000000002 ***
## arg0 0.162477 0.608897 0.267 0.790
## arg1 -0.206122 0.608573 -0.339 0.735
## op_count:arg0 0.565559 0.030445 18.576 <0.0000000000000002 ***
## op_count:arg1 -0.002419 0.030044 -0.081 0.936
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 78.61 on 551 degrees of freedom
## Multiple R-squared: 0.8845, Adjusted R-squared: 0.8835
## F-statistic: 844 on 5 and 551 DF, p-value: < 0.00000000000000022
##
## [1] "SDIV" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -220.14 -31.62 -3.88 21.89 328.05
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.40239 14.42850 40.088 <0.0000000000000002 ***
## op_count 9.61489 0.71866 13.379 <0.0000000000000002 ***
## arg0 -0.01446 0.58583 -0.025 0.9803
## arg1 0.09938 0.58926 0.169 0.8661
## op_count:arg0 0.59519 0.02919 20.392 <0.0000000000000002 ***
## op_count:arg1 -0.06669 0.02937 -2.271 0.0235 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 76.45 on 556 degrees of freedom
## Multiple R-squared: 0.9141, Adjusted R-squared: 0.9133
## F-statistic: 1184 on 5 and 556 DF, p-value: < 0.00000000000000022
##
## [1] "MOD" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -215.01 -36.72 -4.86 23.01 385.01
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 574.129830 15.941266 36.015 <0.0000000000000002 ***
## op_count 7.589832 0.802608 9.456 <0.0000000000000002 ***
## arg0 0.299703 0.623680 0.481 0.631
## arg1 0.015903 0.630077 0.025 0.980
## op_count:arg0 0.512596 0.031602 16.220 <0.0000000000000002 ***
## op_count:arg1 0.009852 0.031631 0.311 0.756
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 83.05 on 551 degrees of freedom
## Multiple R-squared: 0.8769, Adjusted R-squared: 0.8758
## F-statistic: 785.1 on 5 and 551 DF, p-value: < 0.00000000000000022
##
## [1] "SMOD" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -210.96 -35.17 -4.89 18.84 338.03
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 577.270870 14.764950 39.097 <0.0000000000000002 ***
## op_count 11.249794 0.737629 15.251 <0.0000000000000002 ***
## arg0 0.216021 0.563431 0.383 0.702
## arg1 -0.017169 0.612679 -0.028 0.978
## op_count:arg0 0.465614 0.028543 16.313 <0.0000000000000002 ***
## op_count:arg1 -0.007943 0.030588 -0.260 0.795
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 74.88 on 544 degrees of freedom
## Multiple R-squared: 0.9098, Adjusted R-squared: 0.909
## F-statistic: 1098 on 5 and 544 DF, p-value: < 0.00000000000000022
##
## [1] "EXP" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1573.24 -67.27 -7.66 96.62 1071.80
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 583.62560053 45.26921933 12.892 < 0.0000000000000002 ***
## op_count 15.38777608 2.26637174 6.790 0.0000000000289 ***
## arg0 0.00000236 1.92046289 0.000 1.000
## arg1 0.01223918 1.88958919 0.006 0.995
## op_count:arg0 0.19912700 0.09625874 2.069 0.039 *
## op_count:arg1 35.13345187 0.09406292 373.510 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 259 on 556 degrees of freedom
## Multiple R-squared: 0.9993, Adjusted R-squared: 0.9993
## F-statistic: 1.623e+05 on 5 and 556 DF, p-value: < 0.00000000000000022
##
## [1] "SIGNEXTEND" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.879 -3.961 -1.682 4.262 22.349
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 577.9385056 1.0665274 541.888 <0.0000000000000002 ***
## op_count 2.4008585 0.0541124 44.368 <0.0000000000000002 ***
## arg0 -0.0191173 0.0424845 -0.450 0.653
## arg1 0.0033480 0.0442296 0.076 0.940
## op_count:arg0 0.0004963 0.0021667 0.229 0.819
## op_count:arg1 -0.0003585 0.0022379 -0.160 0.873
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.521 on 494 degrees of freedom
## Multiple R-squared: 0.967, Adjusted R-squared: 0.9667
## F-statistic: 2895 on 5 and 494 DF, p-value: < 0.00000000000000022
##
## [1] "LT" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.378 -4.918 -1.845 4.813 28.624
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.602928 1.460834 396.077 <0.0000000000000002 ***
## op_count 2.606130 0.074842 34.822 <0.0000000000000002 ***
## arg0 0.018923 0.056065 0.338 0.736
## arg1 -0.012648 0.060126 -0.210 0.833
## op_count:arg0 -0.004319 0.002910 -1.484 0.138
## op_count:arg1 -0.004204 0.003059 -1.374 0.170
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.2 on 492 degrees of freedom
## Multiple R-squared: 0.9466, Adjusted R-squared: 0.9461
## F-statistic: 1744 on 5 and 492 DF, p-value: < 0.00000000000000022
##
## [1] "GT" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.875 -4.319 -1.875 4.587 20.770
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.819807 1.051893 550.265 < 0.0000000000000002 ***
## op_count 2.622516 0.054573 48.055 < 0.0000000000000002 ***
## arg0 0.011745 0.044740 0.263 0.793
## arg1 -0.013715 0.044165 -0.311 0.756
## op_count:arg0 -0.002180 0.002296 -0.949 0.343
## op_count:arg1 -0.010781 0.002259 -4.773 0.0000024 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.566 on 487 degrees of freedom
## Multiple R-squared: 0.9668, Adjusted R-squared: 0.9664
## F-statistic: 2835 on 5 and 487 DF, p-value: < 0.00000000000000022
##
## [1] "SLT" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.248 -4.959 -1.973 5.088 44.285
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 579.105789 1.624470 356.489 < 0.0000000000000002 ***
## op_count 4.357391 0.080993 53.799 < 0.0000000000000002 ***
## arg0 0.010014 0.060115 0.167 0.868
## arg1 -0.009199 0.059323 -0.155 0.877
## op_count:arg0 -0.015203 0.003030 -5.018 0.00000073 ***
## op_count:arg1 -0.014272 0.002988 -4.777 0.00000236 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.573 on 492 degrees of freedom
## Multiple R-squared: 0.9753, Adjusted R-squared: 0.9751
## F-statistic: 3892 on 5 and 492 DF, p-value: < 0.00000000000000022
##
## [1] "SGT" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.474 -4.713 -2.440 5.622 23.812
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 577.984463 1.324197 436.479 < 0.0000000000000002 ***
## op_count 4.195808 0.066879 62.737 < 0.0000000000000002 ***
## arg0 0.041294 0.055649 0.742 0.458
## arg1 0.021166 0.050946 0.415 0.678
## op_count:arg0 -0.012439 0.002838 -4.383 0.00001429 ***
## op_count:arg1 -0.012666 0.002572 -4.925 0.00000115 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.556 on 494 degrees of freedom
## Multiple R-squared: 0.9808, Adjusted R-squared: 0.9806
## F-statistic: 5035 on 5 and 494 DF, p-value: < 0.00000000000000022
##
## [1] "EQ" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.187 -4.275 -1.944 5.098 19.785
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 577.974894 1.051700 549.562 <0.0000000000000002 ***
## op_count 2.494828 0.054186 46.042 <0.0000000000000002 ***
## arg0 0.035351 0.041121 0.860 0.390
## arg1 -0.013689 0.043545 -0.314 0.753
## op_count:arg0 -0.001074 0.002106 -0.510 0.610
## op_count:arg1 0.003415 0.002246 1.521 0.129
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.398 on 485 degrees of freedom
## Multiple R-squared: 0.9714, Adjusted R-squared: 0.9711
## F-statistic: 3295 on 5 and 485 DF, p-value: < 0.00000000000000022
##
## [1] "AND" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.287 -5.311 -3.196 4.806 24.303
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 580.7117769 1.2665075 458.514 <0.0000000000000002 ***
## op_count 2.4057293 0.0655051 36.726 <0.0000000000000002 ***
## arg0 0.0016566 0.0507734 0.033 0.974
## arg1 -0.0012790 0.0563176 -0.023 0.982
## op_count:arg0 0.0006575 0.0026031 0.253 0.801
## op_count:arg1 0.0009837 0.0028784 0.342 0.733
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.746 on 485 degrees of freedom
## Multiple R-squared: 0.9518, Adjusted R-squared: 0.9513
## F-statistic: 1913 on 5 and 485 DF, p-value: < 0.00000000000000022
##
## [1] "OR" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.714 -4.064 -2.220 4.661 23.725
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 579.2537865 1.1047206 524.344 <0.0000000000000002 ***
## op_count 2.4167326 0.0570293 42.377 <0.0000000000000002 ***
## arg0 -0.0216681 0.0451782 -0.480 0.632
## arg1 -0.0090137 0.0441048 -0.204 0.838
## op_count:arg0 0.0008269 0.0023170 0.357 0.721
## op_count:arg1 0.0007800 0.0022922 0.340 0.734
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.703 on 487 degrees of freedom
## Multiple R-squared: 0.9655, Adjusted R-squared: 0.9651
## F-statistic: 2723 on 5 and 487 DF, p-value: < 0.00000000000000022
##
## [1] "XOR" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.481 -3.639 -2.191 5.251 12.375
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.1003231 0.9242309 625.493 <0.0000000000000002 ***
## op_count 2.3733311 0.0477935 49.658 <0.0000000000000002 ***
## arg0 0.0073060 0.0359936 0.203 0.839
## arg1 0.0128416 0.0360924 0.356 0.722
## op_count:arg0 -0.0012286 0.0018768 -0.655 0.513
## op_count:arg1 -0.0006296 0.0018876 -0.334 0.739
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.753 on 476 degrees of freedom
## Multiple R-squared: 0.9735, Adjusted R-squared: 0.9732
## F-statistic: 3493 on 5 and 476 DF, p-value: < 0.00000000000000022
##
## [1] "BYTE" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.089 -4.969 -2.787 4.715 25.096
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 579.3323227 1.3014497 445.144 <0.0000000000000002 ***
## op_count 2.3441999 0.0681589 34.393 <0.0000000000000002 ***
## arg0 -0.0162000 0.0512175 -0.316 0.752
## arg1 0.0372265 0.0518346 0.718 0.473
## op_count:arg0 0.0012638 0.0026801 0.472 0.637
## op_count:arg1 -0.0001856 0.0026766 -0.069 0.945
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.504 on 491 degrees of freedom
## Multiple R-squared: 0.952, Adjusted R-squared: 0.9515
## F-statistic: 1948 on 5 and 491 DF, p-value: < 0.00000000000000022
##
## [1] "SHL" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.565 -5.642 -2.870 5.926 28.813
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 579.655651 1.385871 418.261 <0.0000000000000002 ***
## op_count 2.827115 0.071530 39.524 <0.0000000000000002 ***
## arg0 0.032336 0.054959 0.588 0.557
## arg1 -0.022290 0.056429 -0.395 0.693
## op_count:arg0 -0.003729 0.002812 -1.326 0.185
## op_count:arg1 0.002713 0.002936 0.924 0.356
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.126 on 478 degrees of freedom
## Multiple R-squared: 0.9593, Adjusted R-squared: 0.9589
## F-statistic: 2252 on 5 and 478 DF, p-value: < 0.00000000000000022
##
## [1] "SHR" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.685 -4.944 -2.764 5.457 28.722
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.408592 1.105551 523.186 <0.0000000000000002 ***
## op_count 3.598871 0.057192 62.926 <0.0000000000000002 ***
## arg0 0.014584 0.048264 0.302 0.763
## arg1 0.013608 0.048924 0.278 0.781
## op_count:arg0 -0.002279 0.002512 -0.907 0.365
## op_count:arg1 -0.001931 0.002482 -0.778 0.437
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.222 on 479 degrees of freedom
## Multiple R-squared: 0.9803, Adjusted R-squared: 0.9801
## F-statistic: 4773 on 5 and 479 DF, p-value: < 0.00000000000000022
##
## [1] "SAR" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.094 -4.737 -2.588 5.468 25.745
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 577.9291232 1.2870546 449.032 <0.0000000000000002 ***
## op_count 3.2413267 0.0657340 49.310 <0.0000000000000002 ***
## arg0 0.0064030 0.0494902 0.129 0.897
## arg1 0.0300961 0.0477849 0.630 0.529
## op_count:arg0 0.0008397 0.0024976 0.336 0.737
## op_count:arg1 -0.0024235 0.0025052 -0.967 0.334
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.067 on 491 degrees of freedom
## Multiple R-squared: 0.9771, Adjusted R-squared: 0.9768
## F-statistic: 4186 on 5 and 491 DF, p-value: < 0.00000000000000022
##
## [1] "MSTORE" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -41.147 -17.645 -8.465 13.177 91.299
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 538.49306335 4.64730485 115.872 <0.0000000000000002 ***
## op_count 4.18247581 0.24102094 17.353 <0.0000000000000002 ***
## arg0 0.00007922 0.00035986 0.220 0.826
## arg1 -0.00006533 0.00035201 -0.186 0.853
## op_count:arg0 -0.00002109 0.00001861 -1.133 0.258
## op_count:arg1 -0.00001436 0.00001813 -0.792 0.429
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 25.39 on 555 degrees of freedom
## Multiple R-squared: 0.7808, Adjusted R-squared: 0.7789
## F-statistic: 395.5 on 5 and 555 DF, p-value: < 0.00000000000000022
##
## [1] "MSTORE8" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -40.490 -16.985 -6.235 10.502 87.179
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 542.6005643728 4.1425739582 130.982 <0.0000000000000002 ***
## op_count 2.7881764889 0.2130658237 13.086 <0.0000000000000002 ***
## arg0 0.0001352236 0.0003465645 0.390 0.697
## arg1 0.0000003489 0.0003461712 0.001 0.999
## op_count:arg0 0.0000186396 0.0000179046 1.041 0.298
## op_count:arg1 -0.0000102763 0.0000180525 -0.569 0.569
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 23.8 on 549 degrees of freedom
## Multiple R-squared: 0.6889, Adjusted R-squared: 0.686
## F-statistic: 243.1 on 5 and 549 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP1" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.9869 -2.8467 -0.3222 2.4667 13.5398
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 371.7890485 0.7596422 489.427 <0.0000000000000002 ***
## op_count 2.6409571 0.0392920 67.214 <0.0000000000000002 ***
## arg0 0.0254139 0.0299630 0.848 0.397
## arg1 0.0075676 0.0297299 0.255 0.799
## op_count:arg0 -0.0008929 0.0015522 -0.575 0.565
## op_count:arg1 -0.0005829 0.0015470 -0.377 0.706
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.159 on 547 degrees of freedom
## Multiple R-squared: 0.9836, Adjusted R-squared: 0.9834
## F-statistic: 6556 on 5 and 547 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP2" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.9003 -3.0321 -0.3491 2.7900 14.8762
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 373.3509505 0.8085630 461.746 <0.0000000000000002 ***
## op_count 2.5739894 0.0413423 62.260 <0.0000000000000002 ***
## arg0 -0.0310115 0.0329243 -0.942 0.347
## arg1 0.0213232 0.0310393 0.687 0.492
## op_count:arg0 0.0013495 0.0016833 0.802 0.423
## op_count:arg1 -0.0003994 0.0016048 -0.249 0.804
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.29 on 543 degrees of freedom
## Multiple R-squared: 0.9822, Adjusted R-squared: 0.982
## F-statistic: 5984 on 5 and 543 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP3" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.6509 -2.7447 -0.1902 2.6976 13.5704
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 372.8550633 0.7489747 497.821 <0.0000000000000002 ***
## op_count 2.4883866 0.0386062 64.456 <0.0000000000000002 ***
## arg0 0.0292115 0.0303381 0.963 0.336
## arg1 -0.0084383 0.0297095 -0.284 0.776
## op_count:arg0 -0.0003483 0.0015618 -0.223 0.824
## op_count:arg1 0.0015942 0.0015263 1.045 0.297
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.156 on 540 degrees of freedom
## Multiple R-squared: 0.9821, Adjusted R-squared: 0.9819
## F-statistic: 5921 on 5 and 540 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP4" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.6903 -2.9869 -0.0338 2.9056 13.1503
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 372.9784844 0.7456925 500.177 <0.0000000000000002 ***
## op_count 2.6052389 0.0385033 67.663 <0.0000000000000002 ***
## arg0 -0.0238099 0.0307345 -0.775 0.439
## arg1 0.0096281 0.0288345 0.334 0.739
## op_count:arg0 0.0007395 0.0015824 0.467 0.640
## op_count:arg1 -0.0003787 0.0014884 -0.254 0.799
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.011 on 547 degrees of freedom
## Multiple R-squared: 0.9847, Adjusted R-squared: 0.9846
## F-statistic: 7057 on 5 and 547 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP5" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.0918 -2.9646 -0.0172 2.4973 13.2049
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 373.311002 0.776098 481.010 <0.0000000000000002 ***
## op_count 2.667998 0.039906 66.856 <0.0000000000000002 ***
## arg0 -0.001324 0.029144 -0.045 0.964
## arg1 -0.015087 0.031299 -0.482 0.630
## op_count:arg0 0.001398 0.001492 0.937 0.349
## op_count:arg1 0.002661 0.001605 1.658 0.098 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.003 on 542 degrees of freedom
## Multiple R-squared: 0.9862, Adjusted R-squared: 0.9861
## F-statistic: 7747 on 5 and 542 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP6" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.1370 -3.0179 -0.4232 2.7336 15.4866
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 377.00572945 0.86866147 434.008 <0.0000000000000002 ***
## op_count 2.74574046 0.04436929 61.884 <0.0000000000000002 ***
## arg0 0.00856924 0.03296331 0.260 0.795
## arg1 -0.00270351 0.03249891 -0.083 0.934
## op_count:arg0 0.00006177 0.00169084 0.037 0.971
## op_count:arg1 0.00105712 0.00165997 0.637 0.525
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.405 on 537 degrees of freedom
## Multiple R-squared: 0.9838, Adjusted R-squared: 0.9836
## F-statistic: 6505 on 5 and 537 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP7" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.454 -2.821 0.080 2.463 12.368
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 380.9486482 0.7563527 503.665 <0.0000000000000002 ***
## op_count 2.5694321 0.0389885 65.902 <0.0000000000000002 ***
## arg0 -0.0011228 0.0305169 -0.037 0.971
## arg1 -0.0039703 0.0317098 -0.125 0.900
## op_count:arg0 -0.0002951 0.0015735 -0.188 0.851
## op_count:arg1 0.0009683 0.0016276 0.595 0.552
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.018 on 547 degrees of freedom
## Multiple R-squared: 0.9843, Adjusted R-squared: 0.9841
## F-statistic: 6838 on 5 and 547 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP8" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.119 -3.048 -0.082 2.752 11.501
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 373.9636038 0.7413011 504.469 <0.0000000000000002 ***
## op_count 2.6610066 0.0385528 69.022 <0.0000000000000002 ***
## arg0 -0.0208065 0.0305185 -0.682 0.496
## arg1 -0.0219504 0.0314283 -0.698 0.485
## op_count:arg0 0.0011395 0.0015840 0.719 0.472
## op_count:arg1 0.0006051 0.0016050 0.377 0.706
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.226 on 548 degrees of freedom
## Multiple R-squared: 0.9841, Adjusted R-squared: 0.9839
## F-statistic: 6778 on 5 and 548 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP9" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.6250 -3.2012 -0.3387 2.6783 19.5764
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 373.6068964 0.9106912 410.245 <0.0000000000000002 ***
## op_count 2.7058593 0.0466796 57.967 <0.0000000000000002 ***
## arg0 -0.0171035 0.0350401 -0.488 0.626
## arg1 0.0034175 0.0337640 0.101 0.919
## op_count:arg0 0.0002839 0.0018052 0.157 0.875
## op_count:arg1 0.0008708 0.0017478 0.498 0.619
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.516 on 533 degrees of freedom
## Multiple R-squared: 0.9824, Adjusted R-squared: 0.9822
## F-statistic: 5950 on 5 and 533 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP10" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.3984 -3.8306 -0.2306 2.9915 14.9911
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 389.904289 0.999537 390.085 <0.0000000000000002 ***
## op_count 2.720076 0.050308 54.069 <0.0000000000000002 ***
## arg0 -0.014180 0.036898 -0.384 0.7009
## arg1 -0.065848 0.039379 -1.672 0.0951 .
## op_count:arg0 0.001325 0.001867 0.710 0.4783
## op_count:arg1 0.002403 0.002016 1.192 0.2339
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.871 on 502 degrees of freedom
## Multiple R-squared: 0.9803, Adjusted R-squared: 0.9801
## F-statistic: 4990 on 5 and 502 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP11" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.3002 -2.5557 -0.2019 2.5291 12.5583
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 372.4849756 0.7327524 508.337 <0.0000000000000002 ***
## op_count 2.9376666 0.0375802 78.171 <0.0000000000000002 ***
## arg0 -0.0004554 0.0302549 -0.015 0.988
## arg1 0.0158675 0.0303553 0.523 0.601
## op_count:arg0 -0.0004760 0.0015708 -0.303 0.762
## op_count:arg1 -0.0008570 0.0015765 -0.544 0.587
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.031 on 547 degrees of freedom
## Multiple R-squared: 0.9874, Adjusted R-squared: 0.9873
## F-statistic: 8572 on 5 and 547 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP12" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.3103 -2.8221 -0.3495 2.8228 12.0587
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 376.064333 0.708696 530.643 <0.0000000000000002 ***
## op_count 2.877073 0.036833 78.112 <0.0000000000000002 ***
## arg0 0.017691 0.028776 0.615 0.539
## arg1 0.041701 0.029854 1.397 0.163
## op_count:arg0 -0.002190 0.001501 -1.459 0.145
## op_count:arg1 -0.001226 0.001556 -0.788 0.431
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.102 on 547 degrees of freedom
## Multiple R-squared: 0.9861, Adjusted R-squared: 0.986
## F-statistic: 7750 on 5 and 547 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP13" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.8220 -2.6527 -0.3802 2.4525 12.6634
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 377.0698645 0.6836212 551.577 <0.0000000000000002 ***
## op_count 2.5479970 0.0354068 71.964 <0.0000000000000002 ***
## arg0 -0.0180584 0.0286806 -0.630 0.529
## arg1 0.0056051 0.0282538 0.198 0.843
## op_count:arg0 0.0016982 0.0014901 1.140 0.255
## op_count:arg1 -0.0002356 0.0014673 -0.161 0.872
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.897 on 539 degrees of freedom
## Multiple R-squared: 0.9852, Adjusted R-squared: 0.9851
## F-statistic: 7180 on 5 and 539 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP14" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.4663 -3.1660 -0.1667 2.6145 14.2576
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 373.0768413 0.7686329 485.377 <0.0000000000000002 ***
## op_count 2.7960584 0.0395283 70.736 <0.0000000000000002 ***
## arg0 -0.0169727 0.0330369 -0.514 0.608
## arg1 0.0310777 0.0321233 0.967 0.334
## op_count:arg0 -0.0002243 0.0016976 -0.132 0.895
## op_count:arg1 -0.0014886 0.0016336 -0.911 0.363
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.329 on 541 degrees of freedom
## Multiple R-squared: 0.9842, Adjusted R-squared: 0.9841
## F-statistic: 6756 on 5 and 541 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP15" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.3203 -2.9053 -0.4521 2.5118 13.3560
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 394.794310 0.811289 486.626 <0.0000000000000002 ***
## op_count 2.555863 0.041184 62.059 <0.0000000000000002 ***
## arg0 -0.009679 0.031009 -0.312 0.7551
## arg1 0.052926 0.030578 1.731 0.0841 .
## op_count:arg0 -0.000114 0.001585 -0.072 0.9427
## op_count:arg1 -0.002902 0.001559 -1.861 0.0633 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.108 on 535 degrees of freedom
## Multiple R-squared: 0.9823, Adjusted R-squared: 0.9822
## F-statistic: 5945 on 5 and 535 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP16" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.6664 -3.1712 -0.3377 2.4134 17.7316
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 388.9554085 0.8711939 446.462 <0.0000000000000002 ***
## op_count 2.4108623 0.0441614 54.592 <0.0000000000000002 ***
## arg0 -0.0283983 0.0325472 -0.873 0.3833
## arg1 -0.0003882 0.0352200 -0.011 0.9912
## op_count:arg0 0.0028235 0.0016511 1.710 0.0878 .
## op_count:arg1 0.0001942 0.0017899 0.108 0.9136
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.442 on 516 degrees of freedom
## Multiple R-squared: 0.9793, Adjusted R-squared: 0.9791
## F-statistic: 4878 on 5 and 516 DF, p-value: < 0.00000000000000022
##
## [1] "ADDMOD" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -886.05 -69.18 -3.94 48.90 777.75
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 579.58320 47.21158 12.276 < 0.0000000000000002 ***
## op_count 3.42233 2.35385 1.454 0.147
## arg0 -0.18787 1.39437 -0.135 0.893
## arg1 0.15989 1.45260 0.110 0.912
## arg2 -0.01129 1.54765 -0.007 0.994
## op_count:arg0 0.85180 0.06989 12.188 < 0.0000000000000002 ***
## op_count:arg1 0.91436 0.07280 12.560 < 0.0000000000000002 ***
## op_count:arg2 -0.50292 0.07733 -6.504 0.000000000176 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 191.3 on 550 degrees of freedom
## Multiple R-squared: 0.8211, Adjusted R-squared: 0.8188
## F-statistic: 360.5 on 7 and 550 DF, p-value: < 0.00000000000000022
##
## [1] "MULMOD" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -496.34 -66.32 -8.48 49.32 633.47
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 593.68070 34.10858 17.406 < 0.0000000000000002 ***
## op_count 7.03445 1.71437 4.103 0.000046985 ***
## arg0 -0.44130 1.17464 -0.376 0.707
## arg1 -0.09379 1.17381 -0.080 0.936
## arg2 -0.07425 1.15092 -0.065 0.949
## op_count:arg0 0.95842 0.05800 16.525 < 0.0000000000000002 ***
## op_count:arg1 0.87817 0.05869 14.963 < 0.0000000000000002 ***
## op_count:arg2 0.28633 0.05750 4.980 0.000000855 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 153.8 on 544 degrees of freedom
## Multiple R-squared: 0.9283, Adjusted R-squared: 0.9273
## F-statistic: 1006 on 7 and 544 DF, p-value: < 0.00000000000000022
##
## [1] "CALLDATACOPY" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -118.597 -32.111 -7.085 21.461 267.395
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 540.259253283 10.304499776 52.429 <0.0000000000000002 ***
## op_count 6.925268432 0.523265712 13.235 <0.0000000000000002 ***
## arg0 0.000220871 0.000716777 0.308 0.758
## arg1 -0.000339802 0.000742307 -0.458 0.647
## arg2 -0.000250952 0.000680307 -0.369 0.712
## op_count:arg0 -0.000001036 0.000036445 -0.028 0.977
## op_count:arg1 0.000024068 0.000037738 0.638 0.524
## op_count:arg2 0.003248158 0.000034585 93.918 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 49.82 on 572 degrees of freedom
## Multiple R-squared: 0.9908, Adjusted R-squared: 0.9907
## F-statistic: 8792 on 7 and 572 DF, p-value: < 0.00000000000000022
##
## [1] "CODECOPY" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -158.67 -30.26 -10.05 28.94 192.92
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 561.83642017 11.57634715 48.533 <0.0000000000000002 ***
## op_count 9.61617071 0.59198437 16.244 <0.0000000000000002 ***
## arg0 0.00111951 0.00074356 1.506 0.1327
## arg1 -0.00048324 0.00073088 -0.661 0.5088
## arg2 -0.00038903 0.00076554 -0.508 0.6115
## op_count:arg0 -0.00002952 0.00003811 -0.775 0.4389
## op_count:arg1 -0.00006668 0.00003755 -1.776 0.0763 .
## op_count:arg2 0.00309823 0.00003899 79.458 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 52.59 on 582 degrees of freedom
## Multiple R-squared: 0.9892, Adjusted R-squared: 0.9891
## F-statistic: 7635 on 7 and 582 DF, p-value: < 0.00000000000000022
##
## [1] "RETURNDATACOPY" "revm"## Warning in summary.lm(model): essentially perfect fit: summary may be unreliable##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q
## -0.0000000000001699 -0.0000000000000276 -0.0000000000000047 0.0000000000000119
## Max
## 0.0000000000068199
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 292.00000000000039790393203 0.00000000000005349860764
## op_count -0.00000000000000229015611 0.00000000000000276265622
## arg0 0.00000000000000000882981 0.00000000000000000420693
## arg1 -0.00000000000000000240034 0.00000000000000000386330
## arg2 -0.00000000000000000899445 0.00000000000000000378421
## op_count:arg0 -0.00000000000000000035319 0.00000000000000000021724
## op_count:arg1 0.00000000000000000009601 0.00000000000000000019950
## op_count:arg2 0.00000000000000000035978 0.00000000000000000019542
## t value Pr(>|t|)
## (Intercept) 5458085973803366.000 <0.0000000000000002 ***
## op_count -0.829 0.4075
## arg0 2.099 0.0363 *
## arg1 -0.621 0.5346
## arg2 -2.377 0.0178 *
## op_count:arg0 -1.626 0.1045
## op_count:arg1 0.481 0.6305
## op_count:arg2 1.841 0.0661 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0000000000002832 on 592 degrees of freedom
## Multiple R-squared: 0.5007, Adjusted R-squared: 0.4948
## F-statistic: 84.82 on 7 and 592 DF, p-value: < 0.00000000000000022## Warning in summary.lm(model): essentially perfect fit: summary may be unreliable## [1] "DIV" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -261.90 -48.13 -11.98 41.21 360.78
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 436.9046 10.6210 41.14 < 0.0000000000000002 ***
## op_count 12.1071 0.3694 32.77 < 0.0000000000000002 ***
## arg0 5.4110 0.4530 11.94 < 0.0000000000000002 ***
## arg1 3.3760 0.4419 7.64 0.0000000000000964 ***
## op_count:expensiveTRUE 7.4412 0.4678 15.91 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 82.93 on 552 degrees of freedom
## Multiple R-squared: 0.8712, Adjusted R-squared: 0.8703
## F-statistic: 933.5 on 4 and 552 DF, p-value: < 0.00000000000000022
##
## [1] "MOD" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -262.16 -50.14 -6.73 44.62 405.13
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 436.4132 11.4344 38.167 < 0.0000000000000002 ***
## op_count 13.1650 0.4042 32.571 < 0.0000000000000002 ***
## arg0 5.0256 0.4718 10.652 < 0.0000000000000002 ***
## arg1 3.4165 0.4742 7.204 0.00000000000193 ***
## op_count:expensiveTRUE 6.6226 0.4975 13.311 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 87.75 on 552 degrees of freedom
## Multiple R-squared: 0.8623, Adjusted R-squared: 0.8613
## F-statistic: 864.3 on 4 and 552 DF, p-value: < 0.00000000000000022
##
## [1] "SDIV" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -255.18 -47.40 -14.02 43.78 348.01
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 441.0153 10.3136 42.760 < 0.0000000000000002 ***
## op_count 14.5387 0.3740 38.869 < 0.0000000000000002 ***
## arg0 5.2015 0.4504 11.549 < 0.0000000000000002 ***
## arg1 2.9989 0.4409 6.802 0.0000000000267 ***
## op_count:expensiveTRUE 7.9530 0.4597 17.300 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 81.47 on 557 degrees of freedom
## Multiple R-squared: 0.9023, Adjusted R-squared: 0.9016
## F-statistic: 1286 on 4 and 557 DF, p-value: < 0.00000000000000022
##
## [1] "SMOD" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -212.37 -47.27 -10.73 41.62 375.27
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 464.0562 10.6934 43.40 < 0.0000000000000002 ***
## op_count 15.9662 0.3518 45.39 < 0.0000000000000002 ***
## arg0 4.7765 0.4342 11.00 < 0.0000000000000002 ***
## arg1 2.5081 0.4544 5.52 0.0000000525 ***
## op_count:expensiveTRUE 5.5801 0.4604 12.12 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 81.11 on 545 degrees of freedom
## Multiple R-squared: 0.894, Adjusted R-squared: 0.8932
## F-statistic: 1149 on 4 and 545 DF, p-value: < 0.00000000000000022
##
## [1] "ADDMOD" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -950.99 -102.36 -17.27 66.83 867.72
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 272.4924 30.1204 9.047 <0.0000000000000002 ***
## op_count 9.6852 1.0688 9.062 <0.0000000000000002 ***
## arg0 8.0595 0.8813 9.145 <0.0000000000000002 ***
## arg1 9.1398 0.9184 9.952 <0.0000000000000002 ***
## arg2 0.3962 1.0167 0.390 0.697
## op_count:expensiveTRUE 21.9140 1.1210 19.549 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 189.7 on 552 degrees of freedom
## Multiple R-squared: 0.8233, Adjusted R-squared: 0.8217
## F-statistic: 514.6 on 5 and 552 DF, p-value: < 0.00000000000000022
##
## [1] "MULMOD" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -422.06 -113.92 -21.88 99.05 845.65
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 103.3492 27.4697 3.762 0.000187 ***
## op_count 27.8311 1.2110 22.982 < 0.0000000000000002 ***
## arg0 10.6671 0.8922 11.955 < 0.0000000000000002 ***
## arg1 10.0156 0.8928 11.219 < 0.0000000000000002 ***
## arg2 9.2059 0.9112 10.103 < 0.0000000000000002 ***
## op_count:expensiveTRUE 16.4387 1.2299 13.366 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 185.8 on 546 degrees of freedom
## Multiple R-squared: 0.8949, Adjusted R-squared: 0.894
## F-statistic: 929.9 on 5 and 546 DF, p-value: < 0.00000000000000022proceed_with_opcodes = unique(first_pass[which(first_pass$has_impacting == 'TRUE'), 'opcode'])
models_with_args_automatic = first_pass[which(first_pass$has_impacting == 'TRUE'), c('opcode', 'env')]
models_with_expensive_automatic = first_pass[which(!is.na(first_pass$expensive_ns)), c('opcode', 'env')]
first_pass[which(first_pass$has_impacting == 'TRUE'), ]## opcode env has_significant has_impacting estimate_marginal_ns
## 26 DIV revm TRUE TRUE 6.59813831183103
## 27 SDIV revm TRUE TRUE 9.61489038110278
## 28 MOD revm TRUE TRUE 7.58983240683187
## 29 SMOD revm TRUE TRUE 11.2497935242536
## 30 EXP revm TRUE TRUE 15.3877760768352
## 62 ADDMOD revm TRUE TRUE 3.42232916262161
## 63 MULMOD revm TRUE TRUE 7.03445025225489
## 64 CALLDATACOPY revm TRUE TRUE 6.92526843170865
## 65 CODECOPY revm TRUE TRUE 9.61617071032377
## arg0_ns arg1_ns arg2_ns expensive_ns
## 26 0.565559437245124 <NA> <NA> 7.4411977916547
## 27 0.595188150328579 <NA> <NA> 7.95304115084793
## 28 0.512595716230183 <NA> <NA> 6.6225636265391
## 29 0.465613812224098 <NA> <NA> <NA>
## 30 <NA> 35.133451871341 <NA> <NA>
## 62 <NA> 0.914357862683964 <NA> 21.9140019413379
## 63 0.958420714710887 0.878170949869229 <NA> 16.4386842908286
## 64 <NA> <NA> 0.00324815767990504 <NA>
## 65 <NA> <NA> 0.00309823088630512 <NA>
## arg0_ns_raw arg1_ns_raw arg2_ns_raw
## 26 0.565559437245124 -0.00241905446431549 <NA>
## 27 0.595188150328579 -0.0666929401571928 <NA>
## 28 0.512595716230183 0.00985196760977073 <NA>
## 29 0.465613812224098 -0.00794251961244524 <NA>
## 30 0.199126997414683 35.133451871341 <NA>
## 62 0.85180210148405 0.914357862683964 -0.50292407970811
## 63 0.958420714710887 0.878170949869229 0.286331944449526
## 64 -0.00000103585949809118 0.0000240683762641382 0.00324815767990504
## 65 -0.0000295190588730632 -0.0000666776821671564 0.00309823088630512
## expensive_ns_raw
## 26 7.4411977916547
## 27 7.95304115084793
## 28 6.6225636265391
## 29 5.58011146072
## 30 <NA>
## 62 21.9140019413379
## 63 16.4386842908286
## 64 <NA>
## 65 <NA>
## arg0_ns_p
## 26 0.00000000000000000000000000000000000000000000000000000000000356462080776181
## 27 0.00000000000000000000000000000000000000000000000000000000000000000000195267240103145
## 28 0.00000000000000000000000000000000000000000000000117768267045346
## 29 0.000000000000000000000000000000000000000000000000541222507731715
## 30 0.0390392884885951
## 62 0.00000000000000000000000000000203856388810974
## 63 0.0000000000000000000000000000000000000000000000000520282291590146
## 64 0.977335263595949
## 65 0.438906299736389
## arg1_ns_p
## 26 0.935854857474513
## 27 0.0235185439403219
## 28 0.755568124386772
## 29 0.795220032552234
## 30 0
## 62 0.0000000000000000000000000000000543998736482254
## 63 0.00000000000000000000000000000000000000000120872236735034
## 64 0.523879935225416
## 65 0.0762762860516619
## arg2_ns_p
## 26 <NA>
## 27 <NA>
## 28 <NA>
## 29 <NA>
## 30 <NA>
## 62 0.000000000176417102430788
## 63 0.000000854946098072246
## 64 0
## 65 1.27679851274989e-314
## expensive_ns_p
## 26 0.000000000000000000000000000000000000000000000034999990739389
## 27 0.00000000000000000000000000000000000000000000000000000549949820477157
## 28 0.0000000000000000000000000000000000293761076143241
## 29 0.00000000000000000000000000000421431567627815
## 30 <NA>
## 62 0.0000000000000000000000000000000000000000000000000000000000000000457785370168952
## 63 0.0000000000000000000000000000000000188580300419877
## 64 <NA>
## 65 <NA>We inspect the automatic choice of models, but then coerce the choice
to a fixed list. We drop the division OPCODEs (DIV etc.),
because their arguments only seem to have an indirect impact via the
fact that x / y is trivial if x < y. This makes the
DIV(x, y) appear costlier for large x and cheaper for large
y.
models_with_args = data.frame(opcode="EXP", env=env, arg=1)
first_pass$arg1_ns[is.na(first_pass$arg1_ns) & first_pass$opcode=="EXP" & first_pass$env==env] <- first_pass$arg1_ns_raw[is.na(first_pass$arg1_ns) & first_pass$opcode=="EXP" & first_pass$env==env]
models_with_args = rbind(models_with_args, data.frame(opcode="CALLDATACOPY", env=env, arg=2))
first_pass$arg2_ns[is.na(first_pass$arg2_ns) & first_pass$opcode=="CALLDATACOPY" & first_pass$env==env] <- first_pass$arg2_ns_raw[is.na(first_pass$arg2_ns) & first_pass$opcode=="CALLDATACOPY" & first_pass$env==env]
models_with_args = rbind(models_with_args, data.frame(opcode="CODECOPY", env=env, arg=2))
first_pass$arg2_ns[is.na(first_pass$arg2_ns) & first_pass$opcode=="CODECOPY" & first_pass$env==env] <- first_pass$arg2_ns_raw[is.na(first_pass$arg2_ns) & first_pass$opcode=="CODECOPY" & first_pass$env==env]
models_with_args = rbind(models_with_args, data.frame(opcode="RETURNDATACOPY", env=env, arg=2))
first_pass$arg2_ns[is.na(first_pass$arg2_ns) & first_pass$opcode=="RETURNDATACOPY" & first_pass$env==env] <- first_pass$arg2_ns_raw[is.na(first_pass$arg2_ns) & first_pass$opcode=="RETURNDATACOPY" & first_pass$env==env]
models_with_expensive = data.frame(opcode="DIV", env=env)
models_with_expensive = rbind(models_with_expensive, data.frame(opcode="SDIV", env=env))
models_with_expensive = rbind(models_with_expensive, data.frame(opcode="MOD", env=env))
models_with_expensive = rbind(models_with_expensive, data.frame(opcode="SMOD", env=env))
models_with_expensive = rbind(models_with_expensive, data.frame(opcode="ADDMOD", env=env))
models_with_expensive = rbind(models_with_expensive, data.frame(opcode="MULMOD", env=env))Detailed analysis for selected OPCODEs
We go through all the OPCODEs which turned out to have impacting arguments in the automatic discrimination procedure, and we plot some validation plots to inspect these relationships.
# Takes the results data frame and checks which argument indices (0, 1, etc.)
# turned out to be impacting
get_impact_args_for <- function(df, opcode, env) {
if (opcode %in% nullary_opcodes) {
return(c())
}
args = c()
for (n in 0:2) {
argname = paste0('arg', n, '_ns')
if (!is.na(df[which(df$opcode==opcode & df$env==env), argname])) {
args = c(n, args)
}
}
return(rev(args))
}
# same as `get_impact_args_for` but gets all the argument indices
get_args_for <- function(df, opcode, env) {
if (opcode %in% unary_opcodes) {
c(0)
} else if (opcode %in% binary_opcodes) {
c(0, 1)
} else if (opcode %in% ternary_opcodes) {
c(0, 1, 2)
}
}
# Builds a final model formula to estimate, based on whether the arguments
# came out impactful from the automatic discrimination process.
get_model_formula_for <- function(df, opcode, env) {
args = get_args_for(df, opcode, env)
argnames = paste0('arg', args)
args_formula = paste0(argnames, collapse=' + ')
impact_args = get_impact_args_for(df, opcode, env)
if (opcode %in% nullary_opcodes) {
as.formula('measure_total_time_ns ~ op_count')
} else if (is.null(impact_args)) {
as.formula(paste0('measure_total_time_ns ~ op_count + ', args_formula))
} else {
arg_op_count_names = paste0('arg', impact_args, ':op_count')
arg_op_counts_formula = paste0(arg_op_count_names, collapse=' + ')
as.formula(paste0('measure_total_time_ns ~ op_count + ', args_formula, ' + ', arg_op_counts_formula))
}
}
# Same as `get_model_formula_for` but gauged towards the division OPCODEs specifically.
get_expensive_model_formula_for <- function(df, opcode, env) {
args = get_args_for(df, opcode, env)
argnames = paste0('arg', args)
args_formula = paste0(argnames, collapse=' + ')
as.formula(paste0('measure_total_time_ns ~ op_count + ', args_formula, ' + expensive:op_count'))
}
# Same as `get_model_formula_for` but returns the formula to provide the `aggregate` function with.
get_aggregate_formula_for <- function(df, opcode, env) {
args = get_args_for(df, opcode, env)
argnames = paste0('arg', args)
args_formula = paste0(argnames, collapse=' * ')
as.formula(paste0('measure_total_time_ns ~ op_count * env * opcode * ', args_formula))
}
# Presents the diagnostic plots for a given slice of the data
plot_model <- function(df, opcode, env, use_mean) {
if (missing(use_mean)) {
use_mean = FALSE
}
if (use_mean) {
df = aggregate(get_aggregate_formula_for(df, opcode, env), measurements[which(df$opcode==opcode & df$env==env), ], mean, na.action=na.pass)
}
model = arg_lm(df, opcode, env, get_model_formula_for(first_pass, opcode, env))
print(c(opcode, env))
print(summary(model))
par(mfrow=c(2,2))
plot(model)
plot_data = df[which(df$env == env & df$opcode == opcode & df$op_count == max(df$op_count)), ]
if (opcode %in% binary_opcodes) {
par(mfrow=c(1,1))
decreasing_colors = heat.colors(nrow(plot_data))
plot_data=plot_data[order(plot_data$measure_total_time_ns, decreasing=TRUE), ]
with(plot_data, plot(arg0, arg1, col=decreasing_colors, pch=19))
}
title(main=paste(opcode, env))
}Using the functions defined above, we proceed to plot the diagnostic plots of the arguments models.
for (env in all_envs) {
for (opcode in proceed_with_opcodes) {
plot_model(measurements, opcode, env, use_mean=TRUE)
}
}## [1] "DIV" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -207.27 -32.57 -4.37 20.18 350.27
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 586.09741 13.27566 44.148 <0.0000000000000002 ***
## op_count 6.42048 0.59087 10.866 <0.0000000000000002 ***
## arg0 0.16373 0.63915 0.256 0.798
## arg1 -0.54332 0.37303 -1.457 0.146
## op_count:arg0 0.57701 0.03206 17.999 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 78.76 on 509 degrees of freedom
## Multiple R-squared: 0.8843, Adjusted R-squared: 0.8833
## F-statistic: 972.1 on 4 and 509 DF, p-value: < 0.00000000000000022
## [1] "SDIV" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -210.215 -27.679 -2.912 21.685 303.576
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 594.62171 12.89919 46.098 < 0.0000000000000002 ***
## op_count 8.71576 0.57427 15.177 < 0.0000000000000002 ***
## arg0 0.09302 0.60457 0.154 0.87778
## arg1 -0.97138 0.36348 -2.672 0.00778 **
## op_count:arg0 0.58158 0.03009 19.325 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 74.66 on 497 degrees of freedom
## Multiple R-squared: 0.9156, Adjusted R-squared: 0.9149
## F-statistic: 1347 on 4 and 497 DF, p-value: < 0.00000000000000022
## [1] "MOD" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -210.75 -32.58 -4.02 22.36 385.85
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 572.90998 13.94968 41.070 <0.0000000000000002 ***
## op_count 7.41997 0.62912 11.794 <0.0000000000000002 ***
## arg0 0.23522 0.63706 0.369 0.712
## arg1 0.14925 0.39123 0.381 0.703
## op_count:arg0 0.52918 0.03235 16.360 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 80.64 on 489 degrees of freedom
## Multiple R-squared: 0.883, Adjusted R-squared: 0.882
## F-statistic: 922.3 on 4 and 489 DF, p-value: < 0.00000000000000022
## [1] "SMOD" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -210.57 -33.70 -4.66 16.31 336.04
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.32430 12.26078 47.169 <0.0000000000000002 ***
## op_count 10.99580 0.52920 20.778 <0.0000000000000002 ***
## arg0 0.13312 0.56552 0.235 0.814
## arg1 -0.02126 0.37383 -0.057 0.955
## op_count:arg0 0.47461 0.02889 16.430 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 74.02 on 510 degrees of freedom
## Multiple R-squared: 0.9127, Adjusted R-squared: 0.912
## F-statistic: 1332 on 4 and 510 DF, p-value: < 0.00000000000000022
## [1] "EXP" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1592.32 -64.14 -1.40 92.80 1052.18
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 540.2785 41.9512 12.879 <0.0000000000000002 ***
## op_count 18.1634 1.8830 9.646 <0.0000000000000002 ***
## arg0 2.9507 1.2495 2.362 0.0186 *
## arg1 -0.2989 2.0253 -0.148 0.8827
## op_count:arg1 35.1845 0.1007 349.367 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 261.2 on 494 degrees of freedom
## Multiple R-squared: 0.9993, Adjusted R-squared: 0.9993
## F-statistic: 1.767e+05 on 4 and 494 DF, p-value: < 0.00000000000000022
## [1] "ADDMOD" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -857.71 -122.78 -2.52 115.04 863.01
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 477.73297 41.96307 11.385 < 0.0000000000000002 ***
## op_count 9.72396 1.62652 5.978 0.00000000404769 ***
## arg0 13.36997 0.97714 13.683 < 0.0000000000000002 ***
## arg1 -0.35492 1.68174 -0.211 0.833
## arg2 -8.08706 1.07695 -7.509 0.00000000000024 ***
## op_count:arg1 0.95911 0.08417 11.395 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 222.1 on 552 degrees of freedom
## Multiple R-squared: 0.758, Adjusted R-squared: 0.7558
## F-statistic: 345.7 on 5 and 552 DF, p-value: < 0.00000000000000022
## [1] "MULMOD" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -523.11 -77.89 -12.58 56.04 661.46
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 523.59672 31.92445 16.401 < 0.0000000000000002 ***
## op_count 11.58578 1.50288 7.709 0.000000000000061 ***
## arg0 -0.33694 1.21006 -0.278 0.781
## arg1 -0.29519 1.19939 -0.246 0.806
## arg2 4.57794 0.70735 6.472 0.000000000216788 ***
## op_count:arg0 0.94425 0.05969 15.819 < 0.0000000000000002 ***
## op_count:arg1 0.88719 0.05997 14.794 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 157.2 on 542 degrees of freedom
## Multiple R-squared: 0.9251, Adjusted R-squared: 0.9243
## F-statistic: 1116 on 6 and 542 DF, p-value: < 0.00000000000000022
## [1] "CALLDATACOPY" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -117.637 -31.901 -7.836 21.407 267.661
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 537.19424542 8.14312893 65.969 <0.0000000000000002 ***
## op_count 7.12318935 0.32931057 21.631 <0.0000000000000002 ***
## arg0 0.00020759 0.00044187 0.470 0.639
## arg1 0.00003278 0.00045751 0.072 0.943
## arg2 -0.00023141 0.00067756 -0.342 0.733
## op_count:arg2 0.00324673 0.00003439 94.402 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 49.75 on 574 degrees of freedom
## Multiple R-squared: 0.9908, Adjusted R-squared: 0.9907
## F-statistic: 1.234e+04 on 5 and 574 DF, p-value: < 0.00000000000000022
## [1] "CODECOPY" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -153.530 -31.334 -8.539 29.108 201.408
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 574.29730576 9.09095785 63.172 < 0.0000000000000002 ***
## op_count 8.79580998 0.35914850 24.491 < 0.0000000000000002 ***
## arg0 0.00067052 0.00046549 1.440 0.15028
## arg1 -0.00149388 0.00045919 -3.253 0.00121 **
## arg2 -0.00054072 0.00076215 -0.709 0.47832
## op_count:arg2 0.00310791 0.00003869 80.320 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 52.66 on 584 degrees of freedom
## Multiple R-squared: 0.9892, Adjusted R-squared: 0.9891
## F-statistic: 1.066e+04 on 5 and 584 DF, p-value: < 0.00000000000000022
Producing the final estimates
We’d like to only estimate using the arg-variables in models, where this actually matters to avoid spurious impact of insignificant variables.
We’ll estimate a model with only those argument variables, where they turned out impacting. For those where no argument variable was impacting, we’ll only estimate the marginal increase (corresponding to the constant cost of an OPCODE).
# `results_df` is assumed to have the columns as the `estimates` data frame has (see below)
add_non_arg_model_estimates <- function(model, results_df, env, opcode) {
pure_op_count_coeff = summary(model)$coefficients["op_count", 1]
args_ns = c(NA, NA, NA)
args_ns_stderr = c(NA, NA, NA)
results_df[nrow(results_df) + 1, ] = c(opcode, env, FALSE, FALSE, pure_op_count_coeff, args_ns, NA, args_ns_stderr, NA)
return(results_df)
}
add_arg_model_estimates <- function(model, opcode, env, results_df, df) {
all_coefficients = summary(model)$coefficients
arg_coefficients = all_coefficients[!(row.names(all_coefficients) %in% c("op_count", "(Intercept)", "arg0", "arg1", "arg2")),]
pure_op_count_coeff = all_coefficients["op_count", 1]
# will be filled if any is impacting
args_ns = c(NA, NA, NA)
args_ns_stderr = c(NA, NA, NA)
impact_args = get_impact_args_for(df, opcode, env)
arg_op_count_names = paste0('op_count:arg', impact_args)
args_ns[impact_args + 1] = all_coefficients[arg_op_count_names, 'Estimate']
args_ns_stderr[impact_args + 1] = all_coefficients[arg_op_count_names, 'Std. Error']
results_df[nrow(results_df) + 1, ] = c(opcode, env, TRUE, TRUE, pure_op_count_coeff, args_ns, NA, args_ns_stderr, NA)
return(results_df)
}
add_expensive_model_estimates <- function(model, opcode, env, results_df, df) {
all_coefficients = summary(model)$coefficients
pure_op_count_coeff = all_coefficients["op_count", 1]
args_ns = c(NA, NA, NA)
args_ns_stderr = c(NA, NA, NA)
expensive = all_coefficients['op_count:expensiveTRUE', 'Estimate']
expensive_stderr = all_coefficients['op_count:expensiveTRUE', 'Std. Error']
results_df[nrow(results_df) + 1, ] = c(opcode, env, TRUE, TRUE, pure_op_count_coeff, args_ns, expensive, args_ns_stderr, expensive_stderr)
return(results_df)
}estimates = data.frame(matrix(ncol = 13, nrow = 0))
colnames(estimates) <- c('opcode', 'env', 'has_significant', 'has_impacting', 'estimate_marginal_ns',
'arg0_ns', 'arg1_ns', 'arg2_ns', 'expensive_ns', 'arg0_ns_stderr', 'arg1_ns_stderr', 'arg2_ns_stderr', 'expensive_ns_stderr')
for (env in all_envs) {
for (opcode in all_opcodes) {
is_modeled_with_args = nrow(merge(data.frame(opcode=opcode, env=env), models_with_args)) > 0
is_modeled_with_expensive = nrow(merge(data.frame(opcode=opcode, env=env), models_with_expensive)) > 0
if (is_modeled_with_expensive) {
model = arg_lm(measurements, opcode, env, get_expensive_model_formula_for(first_pass, opcode, env))
estimates = add_expensive_model_estimates(model, opcode, env, estimates, first_pass)
} else if (is_modeled_with_args) {
model = arg_lm(measurements, opcode, env, get_model_formula_for(first_pass, opcode, env))
estimates = add_arg_model_estimates(model, opcode, env, estimates, first_pass)
} else {
model = arg_lm(measurements, opcode, env, get_model_formula_for(first_pass, opcode, env))
estimates = add_non_arg_model_estimates(model, estimates, env, opcode)
}
print(c(opcode, env))
print(summary(model))
}
}## [1] "ADD" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.243 -5.392 -3.273 6.724 15.797
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 579.32285 0.91766 631.306 <0.0000000000000002 ***
## op_count 2.86163 0.02407 118.889 <0.0000000000000002 ***
## arg0 0.01546 0.03311 0.467 0.641
## arg1 -0.01167 0.03189 -0.366 0.715
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.621 on 500 degrees of freedom
## Multiple R-squared: 0.9659, Adjusted R-squared: 0.9657
## F-statistic: 4718 on 3 and 500 DF, p-value: < 0.00000000000000022
##
## [1] "MUL" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.013 -4.814 -2.831 4.984 25.065
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.780438 0.873290 662.759 <0.0000000000000002 ***
## op_count 3.589878 0.023615 152.018 <0.0000000000000002 ***
## arg0 0.040799 0.031477 1.296 0.196
## arg1 -0.003624 0.032365 -0.112 0.911
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.365 on 490 degrees of freedom
## Multiple R-squared: 0.9793, Adjusted R-squared: 0.9792
## F-statistic: 7735 on 3 and 490 DF, p-value: < 0.00000000000000022
##
## [1] "SUB" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.246 -5.068 -2.735 5.427 30.466
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 579.478139 0.931260 622.251 <0.0000000000000002 ***
## op_count 2.765530 0.023758 116.404 <0.0000000000000002 ***
## arg0 -0.009515 0.033557 -0.284 0.777
## arg1 -0.026913 0.033098 -0.813 0.417
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.598 on 501 degrees of freedom
## Multiple R-squared: 0.9644, Adjusted R-squared: 0.9641
## F-statistic: 4518 on 3 and 501 DF, p-value: < 0.00000000000000022
##
## [1] "DIV" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -261.90 -48.13 -11.98 41.21 360.78
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 436.9046 10.6210 41.14 < 0.0000000000000002 ***
## op_count 12.1071 0.3694 32.77 < 0.0000000000000002 ***
## arg0 5.4110 0.4530 11.94 < 0.0000000000000002 ***
## arg1 3.3760 0.4419 7.64 0.0000000000000964 ***
## op_count:expensiveTRUE 7.4412 0.4678 15.91 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 82.93 on 552 degrees of freedom
## Multiple R-squared: 0.8712, Adjusted R-squared: 0.8703
## F-statistic: 933.5 on 4 and 552 DF, p-value: < 0.00000000000000022
##
## [1] "SDIV" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -255.18 -47.40 -14.02 43.78 348.01
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 441.0153 10.3136 42.760 < 0.0000000000000002 ***
## op_count 14.5387 0.3740 38.869 < 0.0000000000000002 ***
## arg0 5.2015 0.4504 11.549 < 0.0000000000000002 ***
## arg1 2.9989 0.4409 6.802 0.0000000000267 ***
## op_count:expensiveTRUE 7.9530 0.4597 17.300 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 81.47 on 557 degrees of freedom
## Multiple R-squared: 0.9023, Adjusted R-squared: 0.9016
## F-statistic: 1286 on 4 and 557 DF, p-value: < 0.00000000000000022
##
## [1] "MOD" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -262.16 -50.14 -6.73 44.62 405.13
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 436.4132 11.4344 38.167 < 0.0000000000000002 ***
## op_count 13.1650 0.4042 32.571 < 0.0000000000000002 ***
## arg0 5.0256 0.4718 10.652 < 0.0000000000000002 ***
## arg1 3.4165 0.4742 7.204 0.00000000000193 ***
## op_count:expensiveTRUE 6.6226 0.4975 13.311 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 87.75 on 552 degrees of freedom
## Multiple R-squared: 0.8623, Adjusted R-squared: 0.8613
## F-statistic: 864.3 on 4 and 552 DF, p-value: < 0.00000000000000022
##
## [1] "SMOD" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -212.37 -47.27 -10.73 41.62 375.27
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 464.0562 10.6934 43.40 < 0.0000000000000002 ***
## op_count 15.9662 0.3518 45.39 < 0.0000000000000002 ***
## arg0 4.7765 0.4342 11.00 < 0.0000000000000002 ***
## arg1 2.5081 0.4544 5.52 0.0000000525 ***
## op_count:expensiveTRUE 5.5801 0.4604 12.12 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 81.11 on 545 degrees of freedom
## Multiple R-squared: 0.894, Adjusted R-squared: 0.8932
## F-statistic: 1149 on 4 and 545 DF, p-value: < 0.00000000000000022
##
## [1] "ADDMOD" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -950.99 -102.36 -17.27 66.83 867.72
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 272.4924 30.1204 9.047 <0.0000000000000002 ***
## op_count 9.6852 1.0688 9.062 <0.0000000000000002 ***
## arg0 8.0595 0.8813 9.145 <0.0000000000000002 ***
## arg1 9.1398 0.9184 9.952 <0.0000000000000002 ***
## arg2 0.3962 1.0167 0.390 0.697
## op_count:expensiveTRUE 21.9140 1.1210 19.549 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 189.7 on 552 degrees of freedom
## Multiple R-squared: 0.8233, Adjusted R-squared: 0.8217
## F-statistic: 514.6 on 5 and 552 DF, p-value: < 0.00000000000000022
##
## [1] "MULMOD" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -422.06 -113.92 -21.88 99.05 845.65
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 103.3492 27.4697 3.762 0.000187 ***
## op_count 27.8311 1.2110 22.982 < 0.0000000000000002 ***
## arg0 10.6671 0.8922 11.955 < 0.0000000000000002 ***
## arg1 10.0156 0.8928 11.219 < 0.0000000000000002 ***
## arg2 9.2059 0.9112 10.103 < 0.0000000000000002 ***
## op_count:expensiveTRUE 16.4387 1.2299 13.366 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 185.8 on 546 degrees of freedom
## Multiple R-squared: 0.8949, Adjusted R-squared: 0.894
## F-statistic: 929.9 on 5 and 546 DF, p-value: < 0.00000000000000022
##
## [1] "EXP" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1568.87 -71.73 -0.50 98.24 1069.40
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 535.97618 39.08558 13.713 <0.0000000000000002 ***
## op_count 18.37172 1.75322 10.479 <0.0000000000000002 ***
## arg0 3.17046 1.16068 2.732 0.0065 **
## arg1 -0.22732 1.89158 -0.120 0.9044
## op_count:arg1 35.14924 0.09403 373.816 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 259.7 on 557 degrees of freedom
## Multiple R-squared: 0.9993, Adjusted R-squared: 0.9993
## F-statistic: 2.017e+05 on 4 and 557 DF, p-value: < 0.00000000000000022
##
## [1] "SIGNEXTEND" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.949 -3.905 -1.692 4.266 22.389
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 577.913171 0.731329 790.224 <0.0000000000000002 ***
## op_count 2.402378 0.019947 120.437 <0.0000000000000002 ***
## arg0 -0.011516 0.026456 -0.435 0.664
## arg1 -0.002179 0.027536 -0.079 0.937
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.51 on 496 degrees of freedom
## Multiple R-squared: 0.967, Adjusted R-squared: 0.9668
## F-statistic: 4844 on 3 and 496 DF, p-value: < 0.00000000000000022
##
## [1] "LT" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.468 -5.328 -1.705 4.796 29.046
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 580.68016 1.01316 573.138 <0.0000000000000002 ***
## op_count 2.46814 0.02651 93.091 <0.0000000000000002 ***
## arg0 -0.04481 0.03567 -1.256 0.210
## arg1 -0.07665 0.03741 -2.049 0.041 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.214 on 494 degrees of freedom
## Multiple R-squared: 0.9462, Adjusted R-squared: 0.9458
## F-statistic: 2894 on 3 and 494 DF, p-value: < 0.00000000000000022
##
## [1] "GT" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.561 -4.563 -1.517 4.621 20.752
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 581.98429 0.76084 764.921 < 0.0000000000000002 ***
## op_count 2.40671 0.02072 116.164 < 0.0000000000000002 ***
## arg0 -0.01713 0.02948 -0.581 0.561
## arg1 -0.17608 0.02857 -6.162 0.0000000015 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.694 on 489 degrees of freedom
## Multiple R-squared: 0.9651, Adjusted R-squared: 0.9649
## F-statistic: 4506 on 3 and 489 DF, p-value: < 0.00000000000000022
##
## [1] "SLT" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.325 -5.528 -0.536 5.354 40.838
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 587.00865 1.13950 515.147 < 0.0000000000000002 ***
## op_count 3.85641 0.02889 133.473 < 0.0000000000000002 ***
## arg0 -0.22794 0.03882 -5.871 0.00000000796 ***
## arg1 -0.23440 0.03779 -6.203 0.00000000117 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.884 on 494 degrees of freedom
## Multiple R-squared: 0.9732, Adjusted R-squared: 0.973
## F-statistic: 5972 on 3 and 494 DF, p-value: < 0.00000000000000022
##
## [1] "SGT" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.768 -5.294 -1.580 5.808 22.667
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 584.35310 0.93827 622.801 < 0.0000000000000002 ***
## op_count 3.78642 0.02489 152.099 < 0.0000000000000002 ***
## arg0 -0.15161 0.03628 -4.179 0.000034639 ***
## arg1 -0.17754 0.03296 -5.386 0.000000111 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.824 on 496 degrees of freedom
## Multiple R-squared: 0.9791, Adjusted R-squared: 0.9789
## F-statistic: 7733 on 3 and 496 DF, p-value: < 0.00000000000000022
##
## [1] "EQ" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.837 -4.454 -2.037 5.106 20.073
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 577.36817 0.72024 801.635 <0.0000000000000002 ***
## op_count 2.53432 0.01978 128.118 <0.0000000000000002 ***
## arg0 0.01979 0.02593 0.763 0.446
## arg1 0.03751 0.02740 1.369 0.172
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.402 on 487 degrees of freedom
## Multiple R-squared: 0.9713, Adjusted R-squared: 0.9711
## F-statistic: 5484 on 3 and 487 DF, p-value: < 0.00000000000000022
##
## [1] "ISZERO" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.743 -3.894 -1.885 5.676 12.698
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.75439 0.54734 1057.405 <0.0000000000000002 ***
## op_count 2.17168 0.01919 113.178 <0.0000000000000002 ***
## arg0 -0.00116 0.02607 -0.044 0.965
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.116 on 474 degrees of freedom
## Multiple R-squared: 0.9643, Adjusted R-squared: 0.9642
## F-statistic: 6405 on 2 and 474 DF, p-value: < 0.00000000000000022
##
## [1] "AND" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.994 -5.404 -3.366 4.808 24.310
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 580.30325 0.88473 655.911 <0.0000000000000002 ***
## op_count 2.43309 0.02483 97.988 <0.0000000000000002 ***
## arg0 0.01131 0.03200 0.354 0.724
## arg1 0.01346 0.03528 0.382 0.703
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.734 on 487 degrees of freedom
## Multiple R-squared: 0.9517, Adjusted R-squared: 0.9514
## F-statistic: 3201 on 3 and 487 DF, p-value: < 0.00000000000000022
##
## [1] "OR" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.833 -4.036 -2.178 4.666 23.726
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.870631 0.772138 749.698 <0.0000000000000002 ***
## op_count 2.442502 0.020908 116.820 <0.0000000000000002 ***
## arg0 -0.009362 0.028531 -0.328 0.743
## arg1 0.002409 0.027955 0.086 0.931
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.692 on 489 degrees of freedom
## Multiple R-squared: 0.9654, Adjusted R-squared: 0.9652
## F-statistic: 4554 on 3 and 489 DF, p-value: < 0.00000000000000022
##
## [1] "XOR" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.638 -3.636 -2.041 5.260 12.372
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.580326 0.629749 918.747 <0.0000000000000002 ***
## op_count 2.341813 0.017708 132.244 <0.0000000000000002 ***
## arg0 -0.011279 0.022963 -0.491 0.624
## arg1 0.002733 0.023128 0.118 0.906
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.746 on 478 degrees of freedom
## Multiple R-squared: 0.9734, Adjusted R-squared: 0.9733
## F-statistic: 5839 on 3 and 478 DF, p-value: < 0.00000000000000022
##
## [1] "NOT" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.081 -4.014 -2.053 3.966 15.925
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.191880 0.533669 1083.429 <0.0000000000000002 ***
## op_count 2.199801 0.017943 122.598 <0.0000000000000002 ***
## arg0 -0.005532 0.023820 -0.232 0.816
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.783 on 478 degrees of freedom
## Multiple R-squared: 0.9692, Adjusted R-squared: 0.9691
## F-statistic: 7518 on 2 and 478 DF, p-value: < 0.00000000000000022
##
## [1] "BYTE" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.895 -4.929 -2.852 4.721 25.094
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 579.068707 0.909065 636.994 <0.0000000000000002 ***
## op_count 2.362403 0.023904 98.828 <0.0000000000000002 ***
## arg0 0.002489 0.032572 0.076 0.939
## arg1 0.034000 0.032582 1.043 0.297
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.492 on 493 degrees of freedom
## Multiple R-squared: 0.952, Adjusted R-squared: 0.9517
## F-statistic: 3258 on 3 and 493 DF, p-value: < 0.00000000000000022
##
## [1] "SHL" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.121 -5.628 -3.087 5.930 28.808
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 579.94144 0.96590 600.418 <0.0000000000000002 ***
## op_count 2.80949 0.02650 106.011 <0.0000000000000002 ***
## arg0 -0.02408 0.03470 -0.694 0.488
## arg1 0.01769 0.03597 0.492 0.623
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.131 on 480 degrees of freedom
## Multiple R-squared: 0.9591, Adjusted R-squared: 0.9588
## F-statistic: 3748 on 3 and 480 DF, p-value: < 0.00000000000000022
##
## [1] "SHR" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.545 -4.930 -2.671 5.476 28.718
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 579.38430 0.77024 752.213 <0.0000000000000002 ***
## op_count 3.53413 0.02290 154.352 <0.0000000000000002 ***
## arg0 -0.01948 0.03106 -0.627 0.531
## arg1 -0.01690 0.03054 -0.553 0.580
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.219 on 481 degrees of freedom
## Multiple R-squared: 0.9803, Adjusted R-squared: 0.9801
## F-statistic: 7963 on 3 and 481 DF, p-value: < 0.00000000000000022
##
## [1] "SAR" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.153 -4.835 -2.597 5.470 25.872
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.354434 0.872625 662.775 <0.0000000000000002 ***
## op_count 3.211861 0.022182 144.799 <0.0000000000000002 ***
## arg0 0.019070 0.030645 0.622 0.534
## arg1 -0.005407 0.030265 -0.179 0.858
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.061 on 493 degrees of freedom
## Multiple R-squared: 0.977, Adjusted R-squared: 0.9769
## F-statistic: 6989 on 3 and 493 DF, p-value: < 0.00000000000000022
##
## [1] "ADDRESS" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.487 -6.205 -4.487 9.154 19.154
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 225.2053 0.5548 406.0 <0.0000000000000002 ***
## op_count 5.8427 0.0294 198.7 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.122 on 513 degrees of freedom
## Multiple R-squared: 0.9872, Adjusted R-squared: 0.9872
## F-statistic: 3.95e+04 on 1 and 513 DF, p-value: < 0.00000000000000022
##
## [1] "ORIGIN" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -17.459 -6.650 -2.650 5.945 31.945
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.64998 0.67455 333.0 <0.0000000000000002 ***
## op_count 5.76031 0.03474 165.8 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.16 on 572 degrees of freedom
## Multiple R-squared: 0.9796, Adjusted R-squared: 0.9796
## F-statistic: 2.75e+04 on 1 and 572 DF, p-value: < 0.00000000000000022
##
## [1] "CALLER" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.018 -5.018 -3.689 6.647 18.647
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.68909 0.47155 476.5 <0.0000000000000002 ***
## op_count 6.71095 0.02467 272.0 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.893 on 532 degrees of freedom
## Multiple R-squared: 0.9929, Adjusted R-squared: 0.9928
## F-statistic: 7.399e+04 on 1 and 532 DF, p-value: < 0.00000000000000022
##
## [1] "CALLVALUE" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.901 -4.770 -2.770 6.165 13.165
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 223.76982 0.39784 562.5 <0.0000000000000002 ***
## op_count 2.73771 0.02058 133.1 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.774 on 527 degrees of freedom
## Multiple R-squared: 0.9711, Adjusted R-squared: 0.971
## F-statistic: 1.77e+04 on 1 and 527 DF, p-value: < 0.00000000000000022
##
## [1] "CALLDATALOAD" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -72.12 -38.70 -18.79 37.15 155.70
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 835.4002959 4.7142393 177.208 <0.0000000000000002 ***
## op_count 3.0992753 0.1597015 19.407 <0.0000000000000002 ***
## arg0 0.0005157 0.0004054 1.272 0.204
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 47.67 on 591 degrees of freedom
## Multiple R-squared: 0.3902, Adjusted R-squared: 0.3882
## F-statistic: 189.1 on 2 and 591 DF, p-value: < 0.00000000000000022
##
## [1] "CALLDATASIZE" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.139 -4.870 -3.139 6.495 13.495
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 223.86993 0.41242 542.8 <0.0000000000000002 ***
## op_count 2.37564 0.02155 110.3 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.106 on 544 degrees of freedom
## Multiple R-squared: 0.9572, Adjusted R-squared: 0.9571
## F-statistic: 1.216e+04 on 1 and 544 DF, p-value: < 0.00000000000000022
##
## [1] "CALLDATACOPY" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -117.637 -31.901 -7.836 21.407 267.661
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 537.19424542 8.14312893 65.969 <0.0000000000000002 ***
## op_count 7.12318935 0.32931057 21.631 <0.0000000000000002 ***
## arg0 0.00020759 0.00044187 0.470 0.639
## arg1 0.00003278 0.00045751 0.072 0.943
## arg2 -0.00023141 0.00067756 -0.342 0.733
## op_count:arg2 0.00324673 0.00003439 94.402 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 49.75 on 574 degrees of freedom
## Multiple R-squared: 0.9908, Adjusted R-squared: 0.9907
## F-statistic: 1.234e+04 on 5 and 574 DF, p-value: < 0.00000000000000022
##
## [1] "CODESIZE" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.906 -4.693 -2.906 7.200 12.200
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 223.90638 0.41048 545.5 <0.0000000000000002 ***
## op_count 2.39289 0.02137 112.0 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.971 on 525 degrees of freedom
## Multiple R-squared: 0.9598, Adjusted R-squared: 0.9597
## F-statistic: 1.254e+04 on 1 and 525 DF, p-value: < 0.00000000000000022
##
## [1] "CODECOPY" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -153.530 -31.334 -8.539 29.108 201.408
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 574.29730576 9.09095785 63.172 < 0.0000000000000002 ***
## op_count 8.79580998 0.35914850 24.491 < 0.0000000000000002 ***
## arg0 0.00067052 0.00046549 1.440 0.15028
## arg1 -0.00149388 0.00045919 -3.253 0.00121 **
## arg2 -0.00054072 0.00076215 -0.709 0.47832
## op_count:arg2 0.00310791 0.00003869 80.320 < 0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 52.66 on 584 degrees of freedom
## Multiple R-squared: 0.9892, Adjusted R-squared: 0.9891
## F-statistic: 1.066e+04 on 5 and 584 DF, p-value: < 0.00000000000000022
##
## [1] "GASPRICE" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.938 -4.938 -2.938 7.020 14.020
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.02166 0.42000 533.4 <0.0000000000000002 ***
## op_count 2.73055 0.02172 125.7 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.174 on 544 degrees of freedom
## Multiple R-squared: 0.9667, Adjusted R-squared: 0.9667
## F-statistic: 1.58e+04 on 1 and 544 DF, p-value: < 0.00000000000000022
##
## [1] "RETURNDATASIZE" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.843 -4.348 -2.843 6.405 13.405
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 223.34759 0.37629 593.5 <0.0000000000000002 ***
## op_count 2.28318 0.01948 117.2 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.514 on 533 degrees of freedom
## Multiple R-squared: 0.9626, Adjusted R-squared: 0.9626
## F-statistic: 1.373e+04 on 1 and 533 DF, p-value: < 0.00000000000000022## Warning in summary.lm(model): essentially perfect fit: summary may be unreliable## [1] "RETURNDATACOPY" "revm"## Warning in summary.lm(model): essentially perfect fit: summary may be unreliable##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q
## -0.0000000000001229 -0.0000000000000279 -0.0000000000000051 0.0000000000000122
## Max
## 0.0000000000068519
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 292.0000000000003979039320 0.0000000000000435395541
## op_count -0.0000000000000039107328 0.0000000000000018249520
## arg0 0.0000000000000000035319 0.0000000000000000026624
## arg1 -0.0000000000000000009601 0.0000000000000000024450
## arg2 -0.0000000000000000087549 0.0000000000000000037825
## op_count:arg2 0.0000000000000000003438 0.0000000000000000001952
## t value Pr(>|t|)
## (Intercept) 6706545487355970.000 <0.0000000000000002 ***
## op_count -2.143 0.0325 *
## arg0 1.327 0.1852
## arg1 -0.393 0.6947
## arg2 -2.315 0.0210 *
## op_count:arg2 1.761 0.0787 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0000000000002834 on 594 degrees of freedom
## Multiple R-squared: 0.5024, Adjusted R-squared: 0.4982
## F-statistic: 120 on 5 and 594 DF, p-value: < 0.00000000000000022
##
## [1] "COINBASE" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.323 -5.665 -3.323 7.506 17.506
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.66493 0.49627 452.7 <0.0000000000000002 ***
## op_count 6.05528 0.02615 231.6 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.26 on 519 degrees of freedom
## Multiple R-squared: 0.9904, Adjusted R-squared: 0.9904
## F-statistic: 5.362e+04 on 1 and 519 DF, p-value: < 0.00000000000000022
##
## [1] "TIMESTAMP" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.885 -4.703 -2.885 6.706 11.706
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 223.70284 0.39470 566.8 <0.0000000000000002 ***
## op_count 2.70607 0.02056 131.6 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.776 on 532 degrees of freedom
## Multiple R-squared: 0.9702, Adjusted R-squared: 0.9702
## F-statistic: 1.733e+04 on 1 and 532 DF, p-value: < 0.00000000000000022
##
## [1] "NUMBER" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.320 -4.683 -2.683 6.498 12.498
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 223.68300 0.38845 575.8 <0.0000000000000002 ***
## op_count 2.65457 0.02027 130.9 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.777 on 541 degrees of freedom
## Multiple R-squared: 0.9694, Adjusted R-squared: 0.9694
## F-statistic: 1.714e+04 on 1 and 541 DF, p-value: < 0.00000000000000022
##
## [1] "DIFFICULTY" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.022 -4.597 -3.022 7.191 12.191
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 223.59744 0.39852 561.1 <0.0000000000000002 ***
## op_count 2.88080 0.02062 139.7 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.918 on 547 degrees of freedom
## Multiple R-squared: 0.9727, Adjusted R-squared: 0.9727
## F-statistic: 1.951e+04 on 1 and 547 DF, p-value: < 0.00000000000000022
##
## [1] "GASLIMIT" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.642 -4.642 -2.739 6.309 13.309
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 223.7389 0.4027 555.5 <0.0000000000000002 ***
## op_count 2.6635 0.0208 128.1 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.919 on 544 degrees of freedom
## Multiple R-squared: 0.9679, Adjusted R-squared: 0.9678
## F-statistic: 1.64e+04 on 1 and 544 DF, p-value: < 0.00000000000000022
##
## [1] "CHAINID" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.841 -4.675 -2.675 6.242 13.242
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 223.67494 0.38702 577.9 <0.0000000000000002 ***
## op_count 2.67220 0.02013 132.7 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.673 on 538 degrees of freedom
## Multiple R-squared: 0.9704, Adjusted R-squared: 0.9703
## F-statistic: 1.762e+04 on 1 and 538 DF, p-value: < 0.00000000000000022
##
## [1] "SELFBALANCE" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.515 -4.157 -2.157 5.164 12.164
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 223.15665 0.34865 640.1 <0.0000000000000002 ***
## op_count 2.24529 0.01793 125.2 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.052 on 531 degrees of freedom
## Multiple R-squared: 0.9673, Adjusted R-squared: 0.9672
## F-statistic: 1.569e+04 on 1 and 531 DF, p-value: < 0.00000000000000022
##
## [1] "POP" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.5524 -4.0902 -0.3184 3.8904 17.0106
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 374.42654 0.56069 667.79 <0.0000000000000002 ***
## op_count 1.97967 0.01913 103.46 <0.0000000000000002 ***
## arg0 -0.01201 0.02505 -0.48 0.632
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.43 on 534 degrees of freedom
## Multiple R-squared: 0.9525, Adjusted R-squared: 0.9523
## F-statistic: 5353 on 2 and 534 DF, p-value: < 0.00000000000000022
##
## [1] "MLOAD" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -83.51 -42.44 -16.98 38.29 161.42
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 855.6543066 5.3174766 160.914 <0.0000000000000002 ***
## op_count 3.4554550 0.1744011 19.813 <0.0000000000000002 ***
## arg0 -0.0005107 0.0004522 -1.129 0.259
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 51.92 on 582 degrees of freedom
## Multiple R-squared: 0.4039, Adjusted R-squared: 0.4018
## F-statistic: 197.1 on 2 and 582 DF, p-value: < 0.00000000000000022
##
## [1] "MSTORE" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -38.309 -18.185 -7.933 13.795 90.176
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 542.8238506 3.2072884 169.247 <0.0000000000000002 ***
## op_count 3.8930379 0.0876173 44.432 <0.0000000000000002 ***
## arg0 -0.0002356 0.0002279 -1.033 0.302
## arg1 -0.0002808 0.0002213 -1.269 0.205
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 25.38 on 557 degrees of freedom
## Multiple R-squared: 0.7802, Adjusted R-squared: 0.779
## F-statistic: 658.9 on 3 and 557 DF, p-value: < 0.00000000000000022
##
## [1] "MSTORE8" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -40.997 -17.088 -6.281 10.547 86.547
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 541.4949453 2.9258195 185.075 <0.0000000000000002 ***
## op_count 2.8609933 0.0821192 34.840 <0.0000000000000002 ***
## arg0 0.0004126 0.0002216 1.862 0.0632 .
## arg1 -0.0001518 0.0002204 -0.689 0.4913
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 23.78 on 551 degrees of freedom
## Multiple R-squared: 0.6881, Adjusted R-squared: 0.6864
## F-statistic: 405.3 on 3 and 551 DF, p-value: < 0.00000000000000022
##
## [1] "JUMP" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.954 -5.482 -3.954 8.282 18.282
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 194.48197 0.48920 397.5 <0.0000000000000002 ***
## op_count 2.81572 0.02532 111.2 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.186 on 535 degrees of freedom
## Multiple R-squared: 0.9585, Adjusted R-squared: 0.9585
## F-statistic: 1.237e+04 on 1 and 535 DF, p-value: < 0.00000000000000022
##
## [1] "JUMPI" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -27.28 -15.61 -10.91 22.73 47.11
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 557.53748 2.04064 273.217 <0.0000000000000002 ***
## op_count 3.95360 0.07238 54.621 <0.0000000000000002 ***
## arg0 0.01526 0.09385 0.163 0.871
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.22 on 525 degrees of freedom
## Multiple R-squared: 0.8504, Adjusted R-squared: 0.8498
## F-statistic: 1492 on 2 and 525 DF, p-value: < 0.00000000000000022
##
## [1] "PC" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.912 -5.225 -3.225 8.431 12.431
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.22528 0.45316 494.8 <0.0000000000000002 ***
## op_count 2.62289 0.02344 111.9 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.643 on 535 degrees of freedom
## Multiple R-squared: 0.959, Adjusted R-squared: 0.9589
## F-statistic: 1.252e+04 on 1 and 535 DF, p-value: < 0.00000000000000022
##
## [1] "MSIZE" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.216 -5.216 -3.216 7.774 12.774
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.23578 0.42967 521.9 <0.0000000000000002 ***
## op_count 2.46602 0.02191 112.6 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.242 on 538 degrees of freedom
## Multiple R-squared: 0.9593, Adjusted R-squared: 0.9592
## F-statistic: 1.267e+04 on 1 and 538 DF, p-value: < 0.00000000000000022
##
## [1] "GAS" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.243 -4.640 -2.243 6.059 14.059
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 223.63967 0.38649 578.6 <0.0000000000000002 ***
## op_count 2.28678 0.01996 114.6 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.673 on 552 degrees of freedom
## Multiple R-squared: 0.9596, Adjusted R-squared: 0.9596
## F-statistic: 1.312e+04 on 1 and 552 DF, p-value: < 0.00000000000000022
##
## [1] "JUMPDEST" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -16.258 -16.258 5.725 6.725 7.759
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 44.240841 0.655292 67.513 <0.0000000000000002 ***
## op_count 0.001127 0.033184 0.034 0.973
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.943 on 545 degrees of freedom
## Multiple R-squared: 2.115e-06, Adjusted R-squared: -0.001833
## F-statistic: 0.001152 on 1 and 545 DF, p-value: 0.9729
##
## [1] "PUSH1" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.233 -4.475 -2.475 6.146 13.146
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 223.47467 0.38171 585.5 <0.0000000000000002 ***
## op_count 2.29193 0.02018 113.6 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.634 on 528 degrees of freedom
## Multiple R-squared: 0.9607, Adjusted R-squared: 0.9606
## F-statistic: 1.29e+04 on 1 and 528 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH2" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.541 -4.541 -3.102 7.178 14.178
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.10221 0.42461 527.8 <0.0000000000000002 ***
## op_count 2.31462 0.02178 106.3 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.164 on 532 degrees of freedom
## Multiple R-squared: 0.955, Adjusted R-squared: 0.9549
## F-statistic: 1.129e+04 on 1 and 532 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH3" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.788 -4.745 -2.788 6.734 12.734
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 223.78766 0.40251 556.0 <0.0000000000000002 ***
## op_count 2.36524 0.02083 113.5 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.83 on 520 degrees of freedom
## Multiple R-squared: 0.9612, Adjusted R-squared: 0.9611
## F-statistic: 1.289e+04 on 1 and 520 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH4" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.857 -4.743 -2.857 7.200 13.200
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 223.74267 0.42081 531.7 <0.0000000000000002 ***
## op_count 2.33716 0.02136 109.4 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.129 on 531 degrees of freedom
## Multiple R-squared: 0.9575, Adjusted R-squared: 0.9574
## F-statistic: 1.197e+04 on 1 and 531 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH5" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.866 -4.798 -2.866 6.668 12.668
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 223.79829 0.40352 554.6 <0.0000000000000002 ***
## op_count 2.36893 0.02059 115.0 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.85 on 543 degrees of freedom
## Multiple R-squared: 0.9606, Adjusted R-squared: 0.9605
## F-statistic: 1.323e+04 on 1 and 543 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH6" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.097 -4.955 -2.955 6.974 12.974
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 223.95550 0.40575 552.0 <0.0000000000000002 ***
## op_count 2.40470 0.02066 116.4 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.91 on 541 degrees of freedom
## Multiple R-squared: 0.9616, Adjusted R-squared: 0.9615
## F-statistic: 1.355e+04 on 1 and 541 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH7" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.127 -5.016 -3.016 6.928 13.928
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.12736 0.41900 534.9 <0.0000000000000002 ***
## op_count 2.46297 0.02169 113.6 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.164 on 540 degrees of freedom
## Multiple R-squared: 0.9598, Adjusted R-squared: 0.9597
## F-statistic: 1.29e+04 on 1 and 540 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH8" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.817 -5.335 -3.335 7.424 14.424
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.33455 0.43857 511.5 <0.0000000000000002 ***
## op_count 2.41608 0.02258 107.0 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.569 on 566 degrees of freedom
## Multiple R-squared: 0.9529, Adjusted R-squared: 0.9528
## F-statistic: 1.145e+04 on 1 and 566 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH9" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.372 -5.372 -3.372 7.538 14.539
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.37175 0.44627 502.8 <0.0000000000000002 ***
## op_count 2.40598 0.02283 105.4 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.495 on 536 degrees of freedom
## Multiple R-squared: 0.954, Adjusted R-squared: 0.9539
## F-statistic: 1.111e+04 on 1 and 536 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH10" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.317 -4.317 -2.526 6.079 12.079
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 223.52564 0.38455 581.3 <0.0000000000000002 ***
## op_count 2.35970 0.02006 117.6 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.708 on 541 degrees of freedom
## Multiple R-squared: 0.9624, Adjusted R-squared: 0.9623
## F-statistic: 1.384e+04 on 1 and 541 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH11" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.037 -4.998 -3.037 7.483 12.483
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.03668 0.41829 535.6 <0.0000000000000002 ***
## op_count 2.43204 0.02158 112.7 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.175 on 553 degrees of freedom
## Multiple R-squared: 0.9583, Adjusted R-squared: 0.9582
## F-statistic: 1.27e+04 on 1 and 553 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH12" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.072 -4.144 -3.072 6.892 11.892
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.07210 0.40266 556.5 <0.0000000000000002 ***
## op_count 2.40238 0.02076 115.7 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.867 on 535 degrees of freedom
## Multiple R-squared: 0.9616, Adjusted R-squared: 0.9615
## F-statistic: 1.339e+04 on 1 and 535 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH13" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.771 -4.771 -3.378 6.426 13.426
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.37800 0.42638 526.2 <0.0000000000000002 ***
## op_count 2.41309 0.02184 110.5 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.17 on 541 degrees of freedom
## Multiple R-squared: 0.9576, Adjusted R-squared: 0.9575
## F-statistic: 1.221e+04 on 1 and 541 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH14" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.888 -4.710 -2.888 6.701 13.701
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 223.71027 0.40420 553.5 <0.0000000000000002 ***
## op_count 2.43925 0.02097 116.3 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.009 on 549 degrees of freedom
## Multiple R-squared: 0.961, Adjusted R-squared: 0.9609
## F-statistic: 1.353e+04 on 1 and 549 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH15" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.922 -4.754 -2.922 7.912 10.662
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 223.75400 0.42618 525.0 <0.0000000000000002 ***
## op_count 2.63893 0.02179 121.1 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.194 on 514 degrees of freedom
## Multiple R-squared: 0.9661, Adjusted R-squared: 0.9661
## F-statistic: 1.466e+04 on 1 and 514 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH16" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.873 -4.517 -2.517 6.305 13.305
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 223.51703 0.37233 600.3 <0.0000000000000002 ***
## op_count 2.34521 0.01913 122.6 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.489 on 545 degrees of freedom
## Multiple R-squared: 0.965, Adjusted R-squared: 0.9649
## F-statistic: 1.503e+04 on 1 and 545 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH17" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.444 -5.340 -3.340 7.109 15.108
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.33956 0.45544 492.6 <0.0000000000000002 ***
## op_count 2.50346 0.02307 108.5 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.498 on 527 degrees of freedom
## Multiple R-squared: 0.9572, Adjusted R-squared: 0.9571
## F-statistic: 1.177e+04 on 1 and 527 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH18" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.863 -4.863 -3.526 6.806 13.806
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.52620 0.45608 492.3 <0.0000000000000002 ***
## op_count 2.51121 0.02341 107.3 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.481 on 526 degrees of freedom
## Multiple R-squared: 0.9563, Adjusted R-squared: 0.9562
## F-statistic: 1.151e+04 on 1 and 526 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH19" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.338 -5.118 -3.228 7.772 12.772
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.33843 0.44047 509.3 <0.0000000000000002 ***
## op_count 2.65931 0.02255 117.9 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.444 on 546 degrees of freedom
## Multiple R-squared: 0.9622, Adjusted R-squared: 0.9621
## F-statistic: 1.39e+04 on 1 and 546 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH20" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.331 -4.749 -3.331 7.460 12.460
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.33062 0.42966 522.1 <0.0000000000000002 ***
## op_count 2.48061 0.02192 113.2 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.201 on 541 degrees of freedom
## Multiple R-squared: 0.9595, Adjusted R-squared: 0.9594
## F-statistic: 1.28e+04 on 1 and 541 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH21" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.629 -4.628 -3.393 7.489 12.489
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.39282 0.43220 519.2 <0.0000000000000002 ***
## op_count 2.67452 0.02208 121.1 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.317 on 544 degrees of freedom
## Multiple R-squared: 0.9643, Adjusted R-squared: 0.9642
## F-statistic: 1.468e+04 on 1 and 544 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH22" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.067 -4.640 -3.067 6.646 12.646
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.06721 0.42188 531.1 <0.0000000000000002 ***
## op_count 2.68576 0.02161 124.3 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.072 on 524 degrees of freedom
## Multiple R-squared: 0.9672, Adjusted R-squared: 0.9671
## F-statistic: 1.545e+04 on 1 and 524 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH23" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.804 -4.804 -2.804 6.616 12.616
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 223.96475 0.39220 571.0 <0.0000000000000002 ***
## op_count 2.69463 0.02028 132.9 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.773 on 542 degrees of freedom
## Multiple R-squared: 0.9702, Adjusted R-squared: 0.9702
## F-statistic: 1.765e+04 on 1 and 542 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH24" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.450 -5.160 -3.450 8.195 13.195
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.1602 0.4441 504.7 <0.0000000000000002 ***
## op_count 2.5097 0.0229 109.6 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.536 on 543 degrees of freedom
## Multiple R-squared: 0.9567, Adjusted R-squared: 0.9567
## F-statistic: 1.201e+04 on 1 and 543 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH25" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.139 -4.735 -3.139 7.563 12.563
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 223.73462 0.40974 546 <0.0000000000000002 ***
## op_count 2.58016 0.02115 122 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.027 on 541 degrees of freedom
## Multiple R-squared: 0.9649, Adjusted R-squared: 0.9649
## F-statistic: 1.489e+04 on 1 and 541 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH26" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.844 -4.844 -3.249 7.454 14.454
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.24923 0.43679 513.4 <0.0000000000000002 ***
## op_count 2.68648 0.02263 118.7 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.441 on 547 degrees of freedom
## Multiple R-squared: 0.9626, Adjusted R-squared: 0.9626
## F-statistic: 1.409e+04 on 1 and 547 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH27" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.596 -4.596 -2.939 7.232 12.232
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 223.93899 0.41022 545.9 <0.0000000000000002 ***
## op_count 2.72191 0.02108 129.1 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.007 on 541 degrees of freedom
## Multiple R-squared: 0.9686, Adjusted R-squared: 0.9685
## F-statistic: 1.667e+04 on 1 and 541 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH28" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.878 -4.645 -2.878 7.238 12.238
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 223.87810 0.40844 548.1 <0.0000000000000002 ***
## op_count 2.65891 0.02109 126.1 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.993 on 539 degrees of freedom
## Multiple R-squared: 0.9672, Adjusted R-squared: 0.9671
## F-statistic: 1.59e+04 on 1 and 539 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH29" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.188 -4.760 -2.760 6.526 11.526
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 223.7596 0.3957 565.4 <0.0000000000000002 ***
## op_count 2.7143 0.0209 129.9 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.81 on 530 degrees of freedom
## Multiple R-squared: 0.9695, Adjusted R-squared: 0.9695
## F-statistic: 1.687e+04 on 1 and 530 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH30" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.699 -4.693 -2.699 5.804 11.804
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 223.69914 0.38244 584.9 <0.0000000000000002 ***
## op_count 2.69980 0.02002 134.9 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.615 on 528 degrees of freedom
## Multiple R-squared: 0.9718, Adjusted R-squared: 0.9717
## F-statistic: 1.819e+04 on 1 and 528 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH31" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.009 -4.936 -3.010 7.527 12.527
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.00950 0.41649 537.9 <0.0000000000000002 ***
## op_count 2.76423 0.02148 128.7 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.061 on 531 degrees of freedom
## Multiple R-squared: 0.9689, Adjusted R-squared: 0.9689
## F-statistic: 1.657e+04 on 1 and 531 DF, p-value: < 0.00000000000000022
##
## [1] "PUSH32" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.116 -5.116 -3.157 7.864 11.864
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 224.11604 0.43590 514.2 <0.0000000000000002 ***
## op_count 2.66803 0.02278 117.1 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.373 on 527 degrees of freedom
## Multiple R-squared: 0.963, Adjusted R-squared: 0.9629
## F-statistic: 1.372e+04 on 1 and 527 DF, p-value: < 0.00000000000000022
##
## [1] "DUP1" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.664 -4.716 -2.291 4.741 23.865
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.20518 0.68919 838.967 <0.0000000000000002 ***
## op_count 2.36473 0.02282 103.631 <0.0000000000000002 ***
## arg0 0.01911 0.03045 0.628 0.531
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.167 on 483 degrees of freedom
## Multiple R-squared: 0.957, Adjusted R-squared: 0.9568
## F-statistic: 5370 on 2 and 483 DF, p-value: < 0.00000000000000022
##
## [1] "DUP2" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.171 -3.968 -1.804 3.975 24.769
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.10436 0.53857 1073.41 <0.0000000000000002 ***
## op_count 2.34107 0.01904 122.94 <0.0000000000000002 ***
## arg0 -0.01503 0.02424 -0.62 0.535
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.157 on 484 degrees of freedom
## Multiple R-squared: 0.969, Adjusted R-squared: 0.9688
## F-statistic: 7558 on 2 and 484 DF, p-value: < 0.00000000000000022
##
## [1] "DUP3" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.509 -3.659 -2.381 4.697 22.847
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.76620 0.55847 1036.350 <0.0000000000000002 ***
## op_count 2.31245 0.01872 123.500 <0.0000000000000002 ***
## arg0 -0.02140 0.02590 -0.826 0.409
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.102 on 486 degrees of freedom
## Multiple R-squared: 0.9691, Adjusted R-squared: 0.969
## F-statistic: 7628 on 2 and 486 DF, p-value: < 0.00000000000000022
##
## [1] "DUP4" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.708 -4.048 -2.048 4.696 19.795
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.65871 0.57338 1009.202 <0.0000000000000002 ***
## op_count 2.28828 0.01964 116.531 <0.0000000000000002 ***
## arg0 0.02469 0.02578 0.958 0.339
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.269 on 478 degrees of freedom
## Multiple R-squared: 0.966, Adjusted R-squared: 0.9659
## F-statistic: 6790 on 2 and 478 DF, p-value: < 0.00000000000000022
##
## [1] "DUP5" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.699 -4.054 -1.500 4.016 18.592
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 577.89352 0.53313 1083.965 <0.0000000000000002 ***
## op_count 2.31359 0.01804 128.272 <0.0000000000000002 ***
## arg0 0.01529 0.02312 0.662 0.509
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.887 on 487 degrees of freedom
## Multiple R-squared: 0.9713, Adjusted R-squared: 0.9712
## F-statistic: 8241 on 2 and 487 DF, p-value: < 0.00000000000000022
##
## [1] "DUP6" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.483 -3.550 -1.975 4.245 12.472
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.069115 0.526448 1098.055 <0.0000000000000002 ***
## op_count 2.316361 0.017284 134.018 <0.0000000000000002 ***
## arg0 -0.003191 0.023886 -0.134 0.894
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.681 on 485 degrees of freedom
## Multiple R-squared: 0.9737, Adjusted R-squared: 0.9736
## F-statistic: 8980 on 2 and 485 DF, p-value: < 0.00000000000000022
##
## [1] "DUP7" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.861 -3.915 -1.942 4.216 27.359
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 582.949026 0.641195 909.16 <0.0000000000000002 ***
## op_count 2.258382 0.021726 103.95 <0.0000000000000002 ***
## arg0 -0.006807 0.029586 -0.23 0.818
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.783 on 478 degrees of freedom
## Multiple R-squared: 0.9576, Adjusted R-squared: 0.9575
## F-statistic: 5404 on 2 and 478 DF, p-value: < 0.00000000000000022
##
## [1] "DUP8" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.114 -4.210 -2.190 4.435 21.473
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 586.74846 0.55664 1054.081 <0.0000000000000002 ***
## op_count 2.24548 0.01952 115.017 <0.0000000000000002 ***
## arg0 0.01923 0.02684 0.717 0.474
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.263 on 480 degrees of freedom
## Multiple R-squared: 0.965, Adjusted R-squared: 0.9649
## F-statistic: 6617 on 2 and 480 DF, p-value: < 0.00000000000000022
##
## [1] "DUP9" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.036 -3.431 -2.160 4.424 13.902
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.80497 0.49800 1162.265 <0.0000000000000002 ***
## op_count 2.26253 0.01707 132.514 <0.0000000000000002 ***
## arg0 -0.02081 0.02320 -0.897 0.37
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.553 on 476 degrees of freedom
## Multiple R-squared: 0.9736, Adjusted R-squared: 0.9735
## F-statistic: 8780 on 2 and 476 DF, p-value: < 0.00000000000000022
##
## [1] "DUP10" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.364 -4.416 -2.847 4.515 27.300
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.74387 0.69052 838.127 <0.0000000000000002 ***
## op_count 2.30630 0.02329 99.019 <0.0000000000000002 ***
## arg0 0.01724 0.02982 0.578 0.563
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.259 on 484 degrees of freedom
## Multiple R-squared: 0.953, Adjusted R-squared: 0.9528
## F-statistic: 4903 on 2 and 484 DF, p-value: < 0.00000000000000022
##
## [1] "DUP11" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.436 -3.680 -2.358 4.492 16.572
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 591.70399 0.56785 1042.006 <0.0000000000000002 ***
## op_count 2.33067 0.01907 122.192 <0.0000000000000002 ***
## arg0 -0.01178 0.02602 -0.453 0.651
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.111 on 473 degrees of freedom
## Multiple R-squared: 0.9693, Adjusted R-squared: 0.9692
## F-statistic: 7478 on 2 and 473 DF, p-value: < 0.00000000000000022
##
## [1] "DUP12" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.322 -3.764 -1.896 4.020 16.806
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.44127 0.50438 1146.825 <0.0000000000000002 ***
## op_count 2.28275 0.01744 130.864 <0.0000000000000002 ***
## arg0 -0.01881 0.02271 -0.828 0.408
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.745 on 489 degrees of freedom
## Multiple R-squared: 0.9722, Adjusted R-squared: 0.9721
## F-statistic: 8563 on 2 and 489 DF, p-value: < 0.00000000000000022
##
## [1] "DUP13" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.320 -4.535 -2.552 4.302 23.256
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 582.23894 0.70144 830.059 <0.0000000000000002 ***
## op_count 2.36085 0.02306 102.375 <0.0000000000000002 ***
## arg0 0.01159 0.03075 0.377 0.706
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.274 on 493 degrees of freedom
## Multiple R-squared: 0.9551, Adjusted R-squared: 0.9549
## F-statistic: 5240 on 2 and 493 DF, p-value: < 0.00000000000000022
##
## [1] "DUP14" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.637 -4.332 -2.555 4.628 24.769
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 583.26218 0.64667 901.953 <0.0000000000000002 ***
## op_count 2.25602 0.02254 100.072 <0.0000000000000002 ***
## arg0 0.01170 0.03027 0.387 0.699
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.009 on 480 degrees of freedom
## Multiple R-squared: 0.9543, Adjusted R-squared: 0.9541
## F-statistic: 5012 on 2 and 480 DF, p-value: < 0.00000000000000022
##
## [1] "DUP15" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.120 -4.890 -1.899 3.889 26.105
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 578.95423 0.75591 765.908 <0.0000000000000002 ***
## op_count 2.34100 0.02424 96.567 <0.0000000000000002 ***
## arg0 -0.00919 0.03423 -0.268 0.788
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.541 on 490 degrees of freedom
## Multiple R-squared: 0.9501, Adjusted R-squared: 0.9499
## F-statistic: 4663 on 2 and 490 DF, p-value: < 0.00000000000000022
##
## [1] "DUP16" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.594 -3.867 -1.789 4.626 18.057
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 600.33146 0.53930 1113.160 <0.0000000000000002 ***
## op_count 2.36854 0.01814 130.582 <0.0000000000000002 ***
## arg0 0.02058 0.02382 0.864 0.388
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.873 on 479 degrees of freedom
## Multiple R-squared: 0.9727, Adjusted R-squared: 0.9726
## F-statistic: 8527 on 2 and 479 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP1" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.9596 -2.7900 -0.3508 2.4845 13.5401
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 372.144770 0.528239 704.500 <0.0000000000000002 ***
## op_count 2.617155 0.014435 181.303 <0.0000000000000002 ***
## arg0 0.012072 0.019049 0.634 0.527
## arg1 -0.001122 0.019014 -0.059 0.953
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.153 on 549 degrees of freedom
## Multiple R-squared: 0.9836, Adjusted R-squared: 0.9835
## F-statistic: 1.096e+04 on 3 and 549 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP2" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.0071 -3.0907 -0.2978 2.7865 14.8701
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 373.11210 0.55366 673.901 <0.0000000000000002 ***
## op_count 2.58929 0.01495 173.164 <0.0000000000000002 ***
## arg0 -0.01044 0.02052 -0.509 0.611
## arg1 0.01552 0.01964 0.791 0.430
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.285 on 545 degrees of freedom
## Multiple R-squared: 0.9822, Adjusted R-squared: 0.9821
## F-statistic: 9996 on 3 and 545 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP3" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.5809 -2.7874 -0.2457 2.7206 13.6519
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 372.54409 0.52432 710.531 <0.0000000000000002 ***
## op_count 2.50906 0.01457 172.190 <0.0000000000000002 ***
## arg0 0.02361 0.01917 1.232 0.219
## arg1 0.01582 0.01861 0.850 0.396
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.153 on 542 degrees of freedom
## Multiple R-squared: 0.982, Adjusted R-squared: 0.9819
## F-statistic: 9883 on 3 and 542 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP4" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.7064 -2.9362 0.0339 2.8519 13.1485
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 372.88617 0.51679 721.542 <0.0000000000000002 ***
## op_count 2.61149 0.01388 188.095 <0.0000000000000002 ***
## arg0 -0.01265 0.01939 -0.652 0.515
## arg1 0.00390 0.01827 0.213 0.831
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.005 on 549 degrees of freedom
## Multiple R-squared: 0.9847, Adjusted R-squared: 0.9846
## F-statistic: 1.18e+04 on 3 and 549 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP5" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.7939 -2.9280 -0.0384 2.5148 13.1593
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 372.32327 0.53530 695.544 <0.0000000000000002 ***
## op_count 2.73361 0.01391 196.511 <0.0000000000000002 ***
## arg0 0.01974 0.01856 1.064 0.288
## arg1 0.02515 0.01978 1.272 0.204
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.008 on 544 degrees of freedom
## Multiple R-squared: 0.9861, Adjusted R-squared: 0.986
## F-statistic: 1.288e+04 on 3 and 544 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP6" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.1545 -3.0397 -0.3612 2.7384 15.4865
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 376.714276 0.593962 634.240 <0.0000000000000002 ***
## op_count 2.764786 0.015309 180.602 <0.0000000000000002 ***
## arg0 0.009468 0.020763 0.456 0.649
## arg1 0.013412 0.020361 0.659 0.510
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.398 on 539 degrees of freedom
## Multiple R-squared: 0.9837, Adjusted R-squared: 0.9837
## F-statistic: 1.087e+04 on 3 and 539 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP7" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.3630 -2.8131 0.0053 2.4642 12.3665
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 380.765047 0.521727 729.817 <0.0000000000000002 ***
## op_count 2.581628 0.013942 185.166 <0.0000000000000002 ***
## arg0 -0.005637 0.019256 -0.293 0.77
## arg1 0.010720 0.019893 0.539 0.59
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.012 on 549 degrees of freedom
## Multiple R-squared: 0.9842, Adjusted R-squared: 0.9842
## F-statistic: 1.143e+04 on 3 and 549 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP8" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.7524 -3.1534 -0.1447 2.8104 11.5939
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 373.544271 0.514206 726.449 <0.0000000000000002 ***
## op_count 2.689155 0.014593 184.278 <0.0000000000000002 ***
## arg0 -0.003884 0.019473 -0.199 0.842
## arg1 -0.012677 0.019796 -0.640 0.522
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.221 on 550 degrees of freedom
## Multiple R-squared: 0.9841, Adjusted R-squared: 0.984
## F-statistic: 1.132e+04 on 3 and 550 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP9" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.4469 -3.1860 -0.3294 2.6931 19.5783
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 373.31280 0.62272 599.489 <0.0000000000000002 ***
## op_count 2.72537 0.01578 172.756 <0.0000000000000002 ***
## arg0 -0.01272 0.02204 -0.577 0.564
## arg1 0.01642 0.02145 0.765 0.444
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.508 on 535 degrees of freedom
## Multiple R-squared: 0.9824, Adjusted R-squared: 0.9823
## F-statistic: 9948 on 3 and 535 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP10" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.0148 -3.7398 -0.1356 3.0816 14.9904
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 388.950219 0.682950 569.515 <0.0000000000000002 ***
## op_count 2.781802 0.017617 157.903 <0.0000000000000002 ***
## arg0 0.006601 0.023002 0.287 0.774
## arg1 -0.029375 0.024999 -1.175 0.241
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.87 on 504 degrees of freedom
## Multiple R-squared: 0.9802, Adjusted R-squared: 0.9801
## F-statistic: 8319 on 3 and 504 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP11" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.3170 -2.5961 -0.1884 2.5263 12.5536
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 372.811333 0.510979 729.601 <0.0000000000000002 ***
## op_count 2.915987 0.014077 207.143 <0.0000000000000002 ***
## arg0 -0.007482 0.019151 -0.391 0.696
## arg1 0.003083 0.019089 0.162 0.872
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.025 on 549 degrees of freedom
## Multiple R-squared: 0.9874, Adjusted R-squared: 0.9873
## F-statistic: 1.433e+04 on 3 and 549 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP12" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.4718 -2.8296 -0.2964 2.8027 12.1951
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 376.95271 0.49788 757.112 <0.0000000000000002 ***
## op_count 2.81737 0.01433 196.565 <0.0000000000000002 ***
## arg0 -0.01464 0.01846 -0.793 0.428
## arg1 0.02328 0.01889 1.232 0.218
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.107 on 549 degrees of freedom
## Multiple R-squared: 0.986, Adjusted R-squared: 0.9859
## F-statistic: 1.289e+04 on 3 and 549 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP13" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.8676 -2.8414 -0.1767 2.4295 12.6560
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 376.698144 0.481075 783.034 <0.0000000000000002 ***
## op_count 2.573168 0.013572 189.589 <0.0000000000000002 ***
## arg0 0.006969 0.018435 0.378 0.706
## arg1 0.002178 0.017923 0.122 0.903
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.894 on 541 degrees of freedom
## Multiple R-squared: 0.9852, Adjusted R-squared: 0.9851
## F-statistic: 1.198e+04 on 3 and 541 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP14" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.4135 -3.2052 -0.2455 2.5695 14.2608
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 373.492736 0.536642 695.982 <0.0000000000000002 ***
## op_count 2.768618 0.015048 183.980 <0.0000000000000002 ***
## arg0 -0.020181 0.020988 -0.962 0.337
## arg1 0.008245 0.020045 0.411 0.681
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.324 on 543 degrees of freedom
## Multiple R-squared: 0.9842, Adjusted R-squared: 0.9841
## F-statistic: 1.128e+04 on 3 and 543 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP15" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.8839 -2.7301 -0.6129 2.4036 13.4924
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 395.604061 0.552101 716.543 <0.0000000000000002 ***
## op_count 2.503428 0.014541 172.162 <0.0000000000000002 ***
## arg0 -0.011716 0.019311 -0.607 0.544
## arg1 0.008355 0.019037 0.439 0.661
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.113 on 537 degrees of freedom
## Multiple R-squared: 0.9822, Adjusted R-squared: 0.9821
## F-statistic: 9881 on 3 and 537 DF, p-value: < 0.00000000000000022
##
## [1] "SWAP16" "revm"
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.3992 -3.2512 -0.3614 2.5301 17.3898
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 388.20546 0.58949 658.539 <0.0000000000000002 ***
## op_count 2.45882 0.01576 156.012 <0.0000000000000002 ***
## arg0 0.01482 0.02048 0.724 0.470
## arg1 0.00350 0.02209 0.158 0.874
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.446 on 518 degrees of freedom
## Multiple R-squared: 0.9792, Adjusted R-squared: 0.979
## F-statistic: 8115 on 3 and 518 DF, p-value: < 0.00000000000000022estimates## opcode env has_significant has_impacting
## 1 ADD revm FALSE FALSE
## 2 MUL revm FALSE FALSE
## 3 SUB revm FALSE FALSE
## 4 DIV revm TRUE TRUE
## 5 SDIV revm TRUE TRUE
## 6 MOD revm TRUE TRUE
## 7 SMOD revm TRUE TRUE
## 8 ADDMOD revm TRUE TRUE
## 9 MULMOD revm TRUE TRUE
## 10 EXP revm TRUE TRUE
## 11 SIGNEXTEND revm FALSE FALSE
## 12 LT revm FALSE FALSE
## 13 GT revm FALSE FALSE
## 14 SLT revm FALSE FALSE
## 15 SGT revm FALSE FALSE
## 16 EQ revm FALSE FALSE
## 17 ISZERO revm FALSE FALSE
## 18 AND revm FALSE FALSE
## 19 OR revm FALSE FALSE
## 20 XOR revm FALSE FALSE
## 21 NOT revm FALSE FALSE
## 22 BYTE revm FALSE FALSE
## 23 SHL revm FALSE FALSE
## 24 SHR revm FALSE FALSE
## 25 SAR revm FALSE FALSE
## 26 ADDRESS revm FALSE FALSE
## 27 ORIGIN revm FALSE FALSE
## 28 CALLER revm FALSE FALSE
## 29 CALLVALUE revm FALSE FALSE
## 30 CALLDATALOAD revm FALSE FALSE
## 31 CALLDATASIZE revm FALSE FALSE
## 32 CALLDATACOPY revm TRUE TRUE
## 33 CODESIZE revm FALSE FALSE
## 34 CODECOPY revm TRUE TRUE
## 35 GASPRICE revm FALSE FALSE
## 36 RETURNDATASIZE revm FALSE FALSE
## 37 RETURNDATACOPY revm TRUE TRUE
## 38 COINBASE revm FALSE FALSE
## 39 TIMESTAMP revm FALSE FALSE
## 40 NUMBER revm FALSE FALSE
## 41 DIFFICULTY revm FALSE FALSE
## 42 GASLIMIT revm FALSE FALSE
## 43 CHAINID revm FALSE FALSE
## 44 SELFBALANCE revm FALSE FALSE
## 45 POP revm FALSE FALSE
## 46 MLOAD revm FALSE FALSE
## 47 MSTORE revm FALSE FALSE
## 48 MSTORE8 revm FALSE FALSE
## 49 JUMP revm FALSE FALSE
## 50 JUMPI revm FALSE FALSE
## 51 PC revm FALSE FALSE
## 52 MSIZE revm FALSE FALSE
## 53 GAS revm FALSE FALSE
## 54 JUMPDEST revm FALSE FALSE
## 55 PUSH1 revm FALSE FALSE
## 56 PUSH2 revm FALSE FALSE
## 57 PUSH3 revm FALSE FALSE
## 58 PUSH4 revm FALSE FALSE
## 59 PUSH5 revm FALSE FALSE
## 60 PUSH6 revm FALSE FALSE
## 61 PUSH7 revm FALSE FALSE
## 62 PUSH8 revm FALSE FALSE
## 63 PUSH9 revm FALSE FALSE
## 64 PUSH10 revm FALSE FALSE
## 65 PUSH11 revm FALSE FALSE
## 66 PUSH12 revm FALSE FALSE
## 67 PUSH13 revm FALSE FALSE
## 68 PUSH14 revm FALSE FALSE
## 69 PUSH15 revm FALSE FALSE
## 70 PUSH16 revm FALSE FALSE
## 71 PUSH17 revm FALSE FALSE
## 72 PUSH18 revm FALSE FALSE
## 73 PUSH19 revm FALSE FALSE
## 74 PUSH20 revm FALSE FALSE
## 75 PUSH21 revm FALSE FALSE
## 76 PUSH22 revm FALSE FALSE
## 77 PUSH23 revm FALSE FALSE
## 78 PUSH24 revm FALSE FALSE
## 79 PUSH25 revm FALSE FALSE
## 80 PUSH26 revm FALSE FALSE
## 81 PUSH27 revm FALSE FALSE
## 82 PUSH28 revm FALSE FALSE
## 83 PUSH29 revm FALSE FALSE
## 84 PUSH30 revm FALSE FALSE
## 85 PUSH31 revm FALSE FALSE
## 86 PUSH32 revm FALSE FALSE
## 87 DUP1 revm FALSE FALSE
## 88 DUP2 revm FALSE FALSE
## 89 DUP3 revm FALSE FALSE
## 90 DUP4 revm FALSE FALSE
## 91 DUP5 revm FALSE FALSE
## 92 DUP6 revm FALSE FALSE
## 93 DUP7 revm FALSE FALSE
## 94 DUP8 revm FALSE FALSE
## 95 DUP9 revm FALSE FALSE
## 96 DUP10 revm FALSE FALSE
## 97 DUP11 revm FALSE FALSE
## 98 DUP12 revm FALSE FALSE
## 99 DUP13 revm FALSE FALSE
## 100 DUP14 revm FALSE FALSE
## 101 DUP15 revm FALSE FALSE
## 102 DUP16 revm FALSE FALSE
## 103 SWAP1 revm FALSE FALSE
## 104 SWAP2 revm FALSE FALSE
## 105 SWAP3 revm FALSE FALSE
## 106 SWAP4 revm FALSE FALSE
## 107 SWAP5 revm FALSE FALSE
## 108 SWAP6 revm FALSE FALSE
## 109 SWAP7 revm FALSE FALSE
## 110 SWAP8 revm FALSE FALSE
## 111 SWAP9 revm FALSE FALSE
## 112 SWAP10 revm FALSE FALSE
## 113 SWAP11 revm FALSE FALSE
## 114 SWAP12 revm FALSE FALSE
## 115 SWAP13 revm FALSE FALSE
## 116 SWAP14 revm FALSE FALSE
## 117 SWAP15 revm FALSE FALSE
## 118 SWAP16 revm FALSE FALSE
## estimate_marginal_ns arg0_ns arg1_ns
## 1 2.86163317701086 <NA> <NA>
## 2 3.58987821341003 <NA> <NA>
## 3 2.76552998426964 <NA> <NA>
## 4 12.1071488054329 <NA> <NA>
## 5 14.5387461452149 <NA> <NA>
## 6 13.1649977135391 <NA> <NA>
## 7 15.9661923910559 <NA> <NA>
## 8 9.6851580674 <NA> <NA>
## 9 27.8311039625004 <NA> <NA>
## 10 18.3717230349195 <NA> 35.1492429667228
## 11 2.40237813313727 <NA> <NA>
## 12 2.46813753030766 <NA> <NA>
## 13 2.40671405956645 <NA> <NA>
## 14 3.85641205572707 <NA> <NA>
## 15 3.78641941709235 <NA> <NA>
## 16 2.53432498011178 <NA> <NA>
## 17 2.17168257851522 <NA> <NA>
## 18 2.4330897059555 <NA> <NA>
## 19 2.44250211657751 <NA> <NA>
## 20 2.34181346131666 <NA> <NA>
## 21 2.19980117951678 <NA> <NA>
## 22 2.36240275833916 <NA> <NA>
## 23 2.80949019349331 <NA> <NA>
## 24 3.53412645512575 <NA> <NA>
## 25 3.2118610114136 <NA> <NA>
## 26 5.84273039496274 <NA> <NA>
## 27 5.76031251146242 <NA> <NA>
## 28 6.71094580807455 <NA> <NA>
## 29 2.73771428571428 <NA> <NA>
## 30 3.09927533563248 <NA> <NA>
## 31 2.37563609803992 <NA> <NA>
## 32 7.12318934827811 <NA> <NA>
## 33 2.39289120780556 <NA> <NA>
## 34 8.79580997778795 <NA> <NA>
## 35 2.73055154505057 <NA> <NA>
## 36 2.28318176726734 <NA> <NA>
## 37 -0.0000000000000039107328336896 <NA> <NA>
## 38 6.05527926878008 <NA> <NA>
## 39 2.70607210254933 <NA> <NA>
## 40 2.65457329209666 <NA> <NA>
## 41 2.88080492377114 <NA> <NA>
## 42 2.66344790627944 <NA> <NA>
## 43 2.67219520384448 <NA> <NA>
## 44 2.24529175791854 <NA> <NA>
## 45 1.97967018577624 <NA> <NA>
## 46 3.45545502333159 <NA> <NA>
## 47 3.89303794182304 <NA> <NA>
## 48 2.86099328640939 <NA> <NA>
## 49 2.8157235845098 <NA> <NA>
## 50 3.9535973155056 <NA> <NA>
## 51 2.62288862923474 <NA> <NA>
## 52 2.46602265008374 <NA> <NA>
## 53 2.28677686088942 <NA> <NA>
## 54 0.00112652652285716 <NA> <NA>
## 55 2.29193320299238 <NA> <NA>
## 56 2.31462003072586 <NA> <NA>
## 57 2.36523609245517 <NA> <NA>
## 58 2.33715838026169 <NA> <NA>
## 59 2.36892872123834 <NA> <NA>
## 60 2.40470461955972 <NA> <NA>
## 61 2.46296598947117 <NA> <NA>
## 62 2.41608101695761 <NA> <NA>
## 63 2.4059829766135 <NA> <NA>
## 64 2.35969794353244 <NA> <NA>
## 65 2.43203863949888 <NA> <NA>
## 66 2.40238057767567 <NA> <NA>
## 67 2.41308596859146 <NA> <NA>
## 68 2.43925061078901 <NA> <NA>
## 69 2.63893444466638 <NA> <NA>
## 70 2.34521295195471 <NA> <NA>
## 71 2.50346441545812 <NA> <NA>
## 72 2.51121436286199 <NA> <NA>
## 73 2.65931077952622 <NA> <NA>
## 74 2.48060745405083 <NA> <NA>
## 75 2.67452259653787 <NA> <NA>
## 76 2.68575969198041 <NA> <NA>
## 77 2.69462962962962 <NA> <NA>
## 78 2.50964826022458 <NA> <NA>
## 79 2.58015936986664 <NA> <NA>
## 80 2.68648148148149 <NA> <NA>
## 81 2.72191348891316 <NA> <NA>
## 82 2.65891146375037 <NA> <NA>
## 83 2.71426966865779 <NA> <NA>
## 84 2.69980222941389 <NA> <NA>
## 85 2.76422865057575 <NA> <NA>
## 86 2.66802515559227 <NA> <NA>
## 87 2.36472716219839 <NA> <NA>
## 88 2.34106630495626 <NA> <NA>
## 89 2.31244596531295 <NA> <NA>
## 90 2.28827873759052 <NA> <NA>
## 91 2.31358824483481 <NA> <NA>
## 92 2.31636057073772 <NA> <NA>
## 93 2.25838230866824 <NA> <NA>
## 94 2.24547580146202 <NA> <NA>
## 95 2.26252627743828 <NA> <NA>
## 96 2.30630259439683 <NA> <NA>
## 97 2.33067404532399 <NA> <NA>
## 98 2.28275281553476 <NA> <NA>
## 99 2.36085385122489 <NA> <NA>
## 100 2.25601566766521 <NA> <NA>
## 101 2.3410043980739 <NA> <NA>
## 102 2.368539503121 <NA> <NA>
## 103 2.61715475534659 <NA> <NA>
## 104 2.58929330295013 <NA> <NA>
## 105 2.5090588100305 <NA> <NA>
## 106 2.61148599922059 <NA> <NA>
## 107 2.7336127596368 <NA> <NA>
## 108 2.76478553261767 <NA> <NA>
## 109 2.58162811666002 <NA> <NA>
## 110 2.68915541512192 <NA> <NA>
## 111 2.72537356359357 <NA> <NA>
## 112 2.78180244884668 <NA> <NA>
## 113 2.91598733585316 <NA> <NA>
## 114 2.81737247252645 <NA> <NA>
## 115 2.57316836412952 <NA> <NA>
## 116 2.76861761357033 <NA> <NA>
## 117 2.50342793266828 <NA> <NA>
## 118 2.45882401450633 <NA> <NA>
## arg2_ns expensive_ns arg0_ns_stderr
## 1 <NA> <NA> <NA>
## 2 <NA> <NA> <NA>
## 3 <NA> <NA> <NA>
## 4 <NA> 7.4411977916547 <NA>
## 5 <NA> 7.95304115084793 <NA>
## 6 <NA> 6.6225636265391 <NA>
## 7 <NA> 5.58011146072 <NA>
## 8 <NA> 21.9140019413379 <NA>
## 9 <NA> 16.4386842908286 <NA>
## 10 <NA> <NA> <NA>
## 11 <NA> <NA> <NA>
## 12 <NA> <NA> <NA>
## 13 <NA> <NA> <NA>
## 14 <NA> <NA> <NA>
## 15 <NA> <NA> <NA>
## 16 <NA> <NA> <NA>
## 17 <NA> <NA> <NA>
## 18 <NA> <NA> <NA>
## 19 <NA> <NA> <NA>
## 20 <NA> <NA> <NA>
## 21 <NA> <NA> <NA>
## 22 <NA> <NA> <NA>
## 23 <NA> <NA> <NA>
## 24 <NA> <NA> <NA>
## 25 <NA> <NA> <NA>
## 26 <NA> <NA> <NA>
## 27 <NA> <NA> <NA>
## 28 <NA> <NA> <NA>
## 29 <NA> <NA> <NA>
## 30 <NA> <NA> <NA>
## 31 <NA> <NA> <NA>
## 32 0.00324673455466849 <NA> <NA>
## 33 <NA> <NA> <NA>
## 34 0.00310790946080824 <NA> <NA>
## 35 <NA> <NA> <NA>
## 36 <NA> <NA> <NA>
## 37 0.000000000000000000343808262507671 <NA> <NA>
## 38 <NA> <NA> <NA>
## 39 <NA> <NA> <NA>
## 40 <NA> <NA> <NA>
## 41 <NA> <NA> <NA>
## 42 <NA> <NA> <NA>
## 43 <NA> <NA> <NA>
## 44 <NA> <NA> <NA>
## 45 <NA> <NA> <NA>
## 46 <NA> <NA> <NA>
## 47 <NA> <NA> <NA>
## 48 <NA> <NA> <NA>
## 49 <NA> <NA> <NA>
## 50 <NA> <NA> <NA>
## 51 <NA> <NA> <NA>
## 52 <NA> <NA> <NA>
## 53 <NA> <NA> <NA>
## 54 <NA> <NA> <NA>
## 55 <NA> <NA> <NA>
## 56 <NA> <NA> <NA>
## 57 <NA> <NA> <NA>
## 58 <NA> <NA> <NA>
## 59 <NA> <NA> <NA>
## 60 <NA> <NA> <NA>
## 61 <NA> <NA> <NA>
## 62 <NA> <NA> <NA>
## 63 <NA> <NA> <NA>
## 64 <NA> <NA> <NA>
## 65 <NA> <NA> <NA>
## 66 <NA> <NA> <NA>
## 67 <NA> <NA> <NA>
## 68 <NA> <NA> <NA>
## 69 <NA> <NA> <NA>
## 70 <NA> <NA> <NA>
## 71 <NA> <NA> <NA>
## 72 <NA> <NA> <NA>
## 73 <NA> <NA> <NA>
## 74 <NA> <NA> <NA>
## 75 <NA> <NA> <NA>
## 76 <NA> <NA> <NA>
## 77 <NA> <NA> <NA>
## 78 <NA> <NA> <NA>
## 79 <NA> <NA> <NA>
## 80 <NA> <NA> <NA>
## 81 <NA> <NA> <NA>
## 82 <NA> <NA> <NA>
## 83 <NA> <NA> <NA>
## 84 <NA> <NA> <NA>
## 85 <NA> <NA> <NA>
## 86 <NA> <NA> <NA>
## 87 <NA> <NA> <NA>
## 88 <NA> <NA> <NA>
## 89 <NA> <NA> <NA>
## 90 <NA> <NA> <NA>
## 91 <NA> <NA> <NA>
## 92 <NA> <NA> <NA>
## 93 <NA> <NA> <NA>
## 94 <NA> <NA> <NA>
## 95 <NA> <NA> <NA>
## 96 <NA> <NA> <NA>
## 97 <NA> <NA> <NA>
## 98 <NA> <NA> <NA>
## 99 <NA> <NA> <NA>
## 100 <NA> <NA> <NA>
## 101 <NA> <NA> <NA>
## 102 <NA> <NA> <NA>
## 103 <NA> <NA> <NA>
## 104 <NA> <NA> <NA>
## 105 <NA> <NA> <NA>
## 106 <NA> <NA> <NA>
## 107 <NA> <NA> <NA>
## 108 <NA> <NA> <NA>
## 109 <NA> <NA> <NA>
## 110 <NA> <NA> <NA>
## 111 <NA> <NA> <NA>
## 112 <NA> <NA> <NA>
## 113 <NA> <NA> <NA>
## 114 <NA> <NA> <NA>
## 115 <NA> <NA> <NA>
## 116 <NA> <NA> <NA>
## 117 <NA> <NA> <NA>
## 118 <NA> <NA> <NA>
## arg1_ns_stderr arg2_ns_stderr expensive_ns_stderr
## 1 <NA> <NA> <NA>
## 2 <NA> <NA> <NA>
## 3 <NA> <NA> <NA>
## 4 <NA> <NA> 0.467750681306078
## 5 <NA> <NA> 0.459707488816463
## 6 <NA> <NA> 0.497526258156315
## 7 <NA> <NA> 0.460408581607353
## 8 <NA> <NA> 1.12096044914081
## 9 <NA> <NA> 1.22993212887081
## 10 0.0940282533542338 <NA> <NA>
## 11 <NA> <NA> <NA>
## 12 <NA> <NA> <NA>
## 13 <NA> <NA> <NA>
## 14 <NA> <NA> <NA>
## 15 <NA> <NA> <NA>
## 16 <NA> <NA> <NA>
## 17 <NA> <NA> <NA>
## 18 <NA> <NA> <NA>
## 19 <NA> <NA> <NA>
## 20 <NA> <NA> <NA>
## 21 <NA> <NA> <NA>
## 22 <NA> <NA> <NA>
## 23 <NA> <NA> <NA>
## 24 <NA> <NA> <NA>
## 25 <NA> <NA> <NA>
## 26 <NA> <NA> <NA>
## 27 <NA> <NA> <NA>
## 28 <NA> <NA> <NA>
## 29 <NA> <NA> <NA>
## 30 <NA> <NA> <NA>
## 31 <NA> <NA> <NA>
## 32 <NA> 0.0000343926656372466 <NA>
## 33 <NA> <NA> <NA>
## 34 <NA> 0.0000386939681244167 <NA>
## 35 <NA> <NA> <NA>
## 36 <NA> <NA> <NA>
## 37 <NA> 0.000000000000000000195185779628588 <NA>
## 38 <NA> <NA> <NA>
## 39 <NA> <NA> <NA>
## 40 <NA> <NA> <NA>
## 41 <NA> <NA> <NA>
## 42 <NA> <NA> <NA>
## 43 <NA> <NA> <NA>
## 44 <NA> <NA> <NA>
## 45 <NA> <NA> <NA>
## 46 <NA> <NA> <NA>
## 47 <NA> <NA> <NA>
## 48 <NA> <NA> <NA>
## 49 <NA> <NA> <NA>
## 50 <NA> <NA> <NA>
## 51 <NA> <NA> <NA>
## 52 <NA> <NA> <NA>
## 53 <NA> <NA> <NA>
## 54 <NA> <NA> <NA>
## 55 <NA> <NA> <NA>
## 56 <NA> <NA> <NA>
## 57 <NA> <NA> <NA>
## 58 <NA> <NA> <NA>
## 59 <NA> <NA> <NA>
## 60 <NA> <NA> <NA>
## 61 <NA> <NA> <NA>
## 62 <NA> <NA> <NA>
## 63 <NA> <NA> <NA>
## 64 <NA> <NA> <NA>
## 65 <NA> <NA> <NA>
## 66 <NA> <NA> <NA>
## 67 <NA> <NA> <NA>
## 68 <NA> <NA> <NA>
## 69 <NA> <NA> <NA>
## 70 <NA> <NA> <NA>
## 71 <NA> <NA> <NA>
## 72 <NA> <NA> <NA>
## 73 <NA> <NA> <NA>
## 74 <NA> <NA> <NA>
## 75 <NA> <NA> <NA>
## 76 <NA> <NA> <NA>
## 77 <NA> <NA> <NA>
## 78 <NA> <NA> <NA>
## 79 <NA> <NA> <NA>
## 80 <NA> <NA> <NA>
## 81 <NA> <NA> <NA>
## 82 <NA> <NA> <NA>
## 83 <NA> <NA> <NA>
## 84 <NA> <NA> <NA>
## 85 <NA> <NA> <NA>
## 86 <NA> <NA> <NA>
## 87 <NA> <NA> <NA>
## 88 <NA> <NA> <NA>
## 89 <NA> <NA> <NA>
## 90 <NA> <NA> <NA>
## 91 <NA> <NA> <NA>
## 92 <NA> <NA> <NA>
## 93 <NA> <NA> <NA>
## 94 <NA> <NA> <NA>
## 95 <NA> <NA> <NA>
## 96 <NA> <NA> <NA>
## 97 <NA> <NA> <NA>
## 98 <NA> <NA> <NA>
## 99 <NA> <NA> <NA>
## 100 <NA> <NA> <NA>
## 101 <NA> <NA> <NA>
## 102 <NA> <NA> <NA>
## 103 <NA> <NA> <NA>
## 104 <NA> <NA> <NA>
## 105 <NA> <NA> <NA>
## 106 <NA> <NA> <NA>
## 107 <NA> <NA> <NA>
## 108 <NA> <NA> <NA>
## 109 <NA> <NA> <NA>
## 110 <NA> <NA> <NA>
## 111 <NA> <NA> <NA>
## 112 <NA> <NA> <NA>
## 113 <NA> <NA> <NA>
## 114 <NA> <NA> <NA>
## 115 <NA> <NA> <NA>
## 116 <NA> <NA> <NA>
## 117 <NA> <NA> <NA>
## 118 <NA> <NA> <NA>write.csv(estimates, paste0("../../local/", env, "_argument_estimated_cost.csv"), quote=FALSE, row.names=FALSE)